JP7827656B2 - System and method for generating a fault tree - Google Patents

Description

The present invention relates to computer techniques for generating fault trees.

When a failure or malfunction (hereinafter referred to as "failure, etc.") occurs in operating equipment or machinery (hereinafter referred to as "equipment, etc."), it is essential to accurately understand the nature and condition of the failure and determine the correct cause in order to properly address the failure, etc. One of the main analysis techniques used for such purposes is Fault Tree Analysis (FTA). Fault tree analysis is a method of systematically organizing and analyzing failure modes, which indicate the nature and condition of the failure, etc., and failure mechanisms, which indicate the causes of the failure, etc., for the equipment, etc., using a tree-structured model.

Today, fault tree analysis and related technologies are widely used in a variety of situations, such as comprehensively predicting potential faults in new equipment during the design and manufacturing stages, and investigating the causes of faults in existing equipment (see, for example, Patent Document 1).

Japanese Patent Application Publication No. 7-262019

Fault tree analysis preferably extracts all possible causes of a failure or other issue (hereinafter referred to as "candidate causes") for the equipment in question without any omissions or duplications (mutually exclusive, collectively exhaustive; MECE). If the extracted candidate causes contain omissions, various problems may arise, such as the correct cause not being identified and the investigation taking longer, or omissions occurring in the investigation work during the design and manufacturing stages. Furthermore, if the extracted candidate causes are duplicated, the same candidate causes will be examined repeatedly, resulting in wasted investigation work.

On the other hand, it is preferable that a moderate number of candidate causes be extracted in a fault tree analysis. If a large number of candidate causes are extracted, the task of identifying the correct cause becomes complicated, and the investigation into the cause of a failure or other issue takes a long time. This has become a particularly urgent issue in today's industrial world, where the analysis of failures and other issues is becoming more difficult due to the increase in complexly configured equipment and other equipment.

However, in fault tree analysis, it is difficult to extract all possible causes of failures and other issues without omissions or duplications, while narrowing them down to an appropriate number using existing technologies, and the development of new technology has been awaited.

In consideration of the above-mentioned problems, the present invention aims to provide a technology that can extract candidate causes of failures, etc., without omissions or duplications during fault tree analysis, while narrowing down the number of candidates to an appropriate number.

The fault tree generation system according to the present invention is a system for generating a fault tree and includes at least a processor and a storage device. The storage device includes at least a mathematical formula database that stores mathematical formulas related to physical phenomena, a variable database that stores variables extracted from technical documents, a variable knowledge database that stores variable knowledge related to variables, and a variable relationship database that stores variable relationships that represent the relationship between variables on the left side of mathematical formulas related to the physical phenomena and variables on the right side of the mathematical formulas. The processor expands the causal relationships of malfunction events based on mathematical formulas stored in the mathematical formula database and generates a mathematical formula-based causal model that represents the causal relationships; based on the generated mathematical formula-based causal model, it generates and outputs a fault tree that combines causal relationships related to any malfunction event from the expanded causal relationships of malfunction events; based on the variable database and variable knowledge database, it generates a variable list of the mathematical formula related to the physical phenomenon, the variables, and variable conditions that are the numerical values that the variables can take; based on the mathematical formula related to the physical phenomenon and the generated variable list and variable conditions, it extracts variable relationships between variables on the left and right sides of the mathematical formula and stores them in the variable relationship database; and when generating the fault tree, it constructs causal relationships related to the malfunction event based on the variable relationships stored in the variable relationship database and reduces the number of branches in the fault tree.

Furthermore, the problems disclosed in this application and the solutions thereto will be made clear in the detailed description and drawings.

This invention makes it possible to narrow down and extract an appropriate number of potential causes of failures, etc., in a fault tree analysis, while extracting all possible causes without omission or duplication.

FIG. 1 is a diagram illustrating an example of the overall configuration of a system including a fault tree generation system according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a mathematical formula database. FIG. 10 is a diagram illustrating an example of a configuration of a variable database. FIG. 2 is a diagram illustrating an example of a configuration of a variable knowledge database. FIG. 2 is a diagram illustrating an example of the configuration of an expression format mathematical expression database. FIG. 10 is a diagram illustrating an example of a configuration of a variable relation database. FIG. 10 is a diagram illustrating an example of a configuration of a variable synonym dictionary database. FIG. 10 is a diagram illustrating an example of a causal model. 10 is a flowchart showing an example of the flow of a mathematical-formula-based causal model generation process. FIG. 10 is a diagram illustrating a process for generating a mathematical expression-based causal model. FIG. 10 is a diagram illustrating a process for generating a mathematical expression-based causal model. FIG. 10 is a diagram illustrating a process for generating a mathematical expression-based causal model. FIG. 10 is a diagram illustrating an example of a mathematical formula-based causal model. FIG. 10 is a diagram illustrating an example of a mathematical formula-based causal model. FIG. 10 is a diagram illustrating an example of a mathematical formula-based causal model. FIG. 1 illustrates an example of a formula-based fault tree. FIG. 10 is a diagram illustrating an example of a correspondence relationship between a mathematical expression and a fault tree generated based on the mathematical expression. FIG. 10 is a diagram illustrating an example of a fault tree in which branches have been appropriately reduced by the fault tree generation system according to the embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a mathematical formula decoding unit and a mathematical formula processing unit. 10 is a flowchart showing an example of the flow of a variable condition generation process executed by a mathematical expression decoding unit. 10 is a flowchart showing an example of the flow of processing executed by a mathematical processing unit. FIG. 10 is a diagram illustrating an example of a setting user interface for a mathematical processing unit. FIG. 10 is a diagram illustrating an example of a setting user interface for a mathematical processing unit. FIG. 24 is a diagram illustrating an example of a fault tree in which the setting contents illustrated in FIG. 23 are reflected. FIG. 10 is a diagram illustrating an example of a method for constructing a variable knowledge database.

In the following description, an "interface apparatus" may refer to one or more interface devices. The one or more interface devices may be at least one of the following:
One or more I/O (Input/Output) interface devices. The I/O (Input/Output) interface devices are interface devices to at least one of an I/O device and a remote display computer. The I/O interface device to the display computer may be a communications interface device. The at least one I/O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device.
One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

In the following description, a "persistent storage device" may refer to one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

Also, in the following description, "storage device" may refer to at least memory, including memory and persistent storage devices.

In the following description, a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a broader processor device such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

In the following description, functions may be described using the expression "yyy unit," but the functions may be realized by one or more computer programs being executed by a processor, by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination of these. When a function is realized by a program being executed by a processor, the specified processing is performed using a storage device and/or interface device, etc., as appropriate, and therefore the function may be considered to be at least part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having that processor. A program may be installed from program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example, and multiple functions may be combined into a single function, or a single function may be divided into multiple functions.

In addition, in the following explanation, processing may be described using a "program" as the subject, but processing described using a program as the subject may also be processing performed by a processor or a device having that processor. Furthermore, two or more programs may be realized as a single program, or one program may be realized as two or more programs.

In the following explanation, information that produces an output in response to an input may be described using expressions such as "xxx table," but this information may be a table of any structure, or a learning model such as a neural network that generates an output in response to an input, a genetic algorithm, or a random forest. Therefore, "xxx table" can also be referred to as "xxx information." In the following explanation, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

In the following description, a "fault tree generation system" may be a system composed of one or more physical computers, or may be a system (e.g., a cloud computing system) implemented on a group of physical computing resources (e.g., a cloud infrastructure). When a fault tree generation system "displays" display information, it may mean displaying the display information on a display device possessed by the computer, or the computer may send the display information to a display computer (in the latter case, the display information is displayed by the display computer).

This embodiment will be described in detail below with reference to the drawings.

In the following explanation, duplicate explanations may be omitted by assigning common symbols to identical or similar components.

Furthermore, when there are multiple elements with the same or similar functions, different subscripts may be added to the same reference numeral to distinguish between the multiple elements. On the other hand, when there is no need to distinguish between the multiple elements, the subscript may be omitted.

<Configuration example of fault tree generation system 100>
First, a configuration example of a fault tree generation system 100 according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram schematically illustrating an example of the configuration of an entire system including the fault tree generation system 100.

(Example of overall system configuration)
The fault tree generation system 100 of this embodiment is a computer system that can generate a fault tree in which candidate causes of failures, etc. are extracted without omission or duplication during analysis, while narrowing down the extracted tree to an appropriate number, and is realized by a computer device or a server device having the components described below.

As shown in FIG. 1, this fault tree generation system 100 is connected to information terminals 20a, 20b, 20c, ... 20n (hereinafter collectively referred to as "information terminals 20") such as laptop PCs, tablets, smartphones, etc. owned by users of the fault tree generation system 100, such as developers and maintenance personnel of facilities, via an appropriate communications network 50 such as the Internet or a dedicated line, so that data can be exchanged between them. The fault tree generation system 100 and the communications network 50 are connected via a wired connection using well-known communications equipment (not shown), but may also be connected wirelessly. The information terminals 20 and the communications network 50 are connected wirelessly, but may also be connected via a wired connection. Each user of the fault tree generation system 100 who owns an information terminal 20 is assigned a unique ID known as a user ID in advance.

This information terminal 20 supplies input data to the fault tree generation system 100. The information terminal 20 also receives output data from the fault tree generation system 100. In other words, each user of the fault tree generation system 100 can obtain various information, including the fault tree generated by the fault tree generation system 100, via the information terminal 20.

In this embodiment, the fault tree generation system 100 has been described as consisting of one device, as shown in FIG. 1. However, for example, the fault tree generation system 100 may also consist of multiple devices.

Furthermore, in this embodiment, as shown in FIG. 1, the fault tree generation system 100 is described as being connected to the information terminal 20 via a communication network 50. However, for example, the fault tree generation system may also include the information terminal 20. Also, for example, the fault tree generation system may be configured to include some or all of the functions performed by the information terminal 20.

Furthermore, other devices, such as a server device that stores various data, may be connected to the fault tree generation system 100 via the communication network 50.

(Example of Hardware Configuration of Fault Tree Generation System 100)
Next, an example of the hardware configuration of the fault tree generation system 100 according to this embodiment will be described.

As described above, the fault tree generation system 100 of this embodiment is realized by a single general-purpose computer device. The following description will be given assuming that the fault tree generation system 100 is realized by a single general-purpose computer device that includes one or more processors 110, one or more storage devices (120, 130), one or more interface devices, and wired or wireless communication lines connecting them.

That is, the fault tree generation system 100 has a storage device consisting of a memory 120 and a persistent storage device 130, an interface device including an input device 140 and an output device 150, a communication device 160, and a processor 110 connected to them.

The persistent storage device 130 is an auxiliary storage device made up of non-volatile storage elements such as flash memory. Specific examples of the persistent storage device 130 include a solid state drive (SSD) and a hard disk drive (HDD). The persistent storage device 130 stores at least a fault tree generation program (not shown). The fault tree generation program is a computer program for implementing the functions required by the fault tree generation system 100. This fault tree generation program holds, for example, various information related to variables.

The fault tree generation program is executed by the processor 110, performing various processes to generate a fault tree (details will be described later).

The fault tree generation program may be installed from the program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium. The fault tree generation program may also be composed of device drivers, an operating system, various application programs located at higher levels, and libraries that provide common functions to these programs. Furthermore, two or more programs may be realized as one fault tree generation program, and one fault tree generation program may be realized as two or more programs.

In addition, the persistent storage device 130 stores at least data related to mathematical formulas, data related to defect information, data related to variables, etc. Details of this data and the databases that store each data will be described later.

Memory 120 is a primary storage device primarily consisting of volatile memory elements such as RAM (Random Access Memory). Memory 120 temporarily stores data representing various information read from persistent storage device 130 and various data acquired from information terminal 20.

The processor 110 is a processor device such as a CPU (Central Processing Unit) and various co-processors. This processor 110 loads the fault tree generation program into memory 120 and executes it, thereby providing overall control of the fault tree generation system 100 itself and also managing the control unit that performs various processes such as calculation processing and judgment processing.

The interface device includes a communication interface device that connects to the communication network 50 and communicates with the information terminal 20, and an I/O interface device consisting of an input device 140 and an output device 150.

(Example of Functional Blocks of Fault Tree Generation System 100)
Next, an example of blocks of various functions included in the fault tree generation system 100 according to this embodiment will be described. Note that each block described below does not represent a hardware configuration, but represents a functional block.

The fault tree generation system 100 is configured with the following functional blocks: a control unit, a memory unit, a user interface unit, and a communication unit (none of which are shown).

The control unit performs various data processing based on user input detected by the user interface unit, data acquired by the communication unit, and the fault tree generation program and data stored in the memory unit. The control unit also functions as an interface between the user interface unit, communication unit, and memory unit.

The control unit has at least the following functional blocks: a top event input unit 210, a mathematical expression-based causal model generation unit 220, a mathematical expression-based fault tree generation unit 230, a malfunction information-based causal model generation unit 240, a malfunction information-based causal model combination unit 250, a mathematical expression interpretation unit 260, and a mathematical expression processing unit 270.

The top event input unit 210 performs processing to accept top event input operations performed by the user of the fault tree generation system 100 via the communication unit or input unit.

The mathematical-formula-based causal model generation unit 220 executes processing to generate causal relationships for mathematical formulas stored in the mathematical formula database 310 based on the relationship between the variables on the left side and the variables on the right side of the formula. The causal relationships generated by the mathematical-formula-based causal model generation unit 220 are referred to as mathematical-formula-based causal models. The processing by which the mathematical-formula-based causal model generation unit 220 generates a mathematical-formula-based causal model is referred to as mathematical-formula-based causal model generation processing. Details of the mathematical-formula-based causal model generation processing will be described later with reference to Figures 9 to 15.

The mathematical-formula-based fault tree generator 230 obtains mathematical-formula-based causal models for the top events received by the top event input unit 210 from the mathematical-formula-based causal model database 320 (described below), and performs processing to generate a mathematical-formula-based fault tree by combining all of the obtained mathematical-formula-based causal models. This processing performed by the mathematical-formula-based fault tree generator 230 is referred to as the mathematical-formula-based fault tree generation processing. Details of the mathematical-formula-based fault tree generation processing will be described later in relation to FIG. 16.

The defect information-based causal model generation unit 240 uses natural language processing to extract the causal relationships of defects contained in the defect information stored in the defect information database 330 (described below), and performs processing to generate a defect information-based causal model.

The malfunction information-based causal model combining unit 250 combines the mathematical expression-based fault tree generated by the mathematical expression-based fault tree generating unit 230 with the malfunction-based causal models stored in the malfunction information-based causal model database 340 (described below) and outputs the combined results.

The formula interpretation unit 260 performs at least the process of generating variable conditions. This process performed by the formula interpretation unit 260 is referred to as the variable condition generation process. Details of the variable condition generation process will be described later with reference to Figures 19 and 20.

The mathematical processing unit 270 uses the variable conditions generated in the variable condition generation process to perform a process to appropriately reduce the branches of the generated fault tree. Details of this process will be described later with reference to Figure 19 and Figures 21 to 24.

The control unit is configured using the processor 110, and these functional blocks can be realized by reading the aforementioned fault tree generation program from the persistent storage device 130 onto the memory 120 and executing it. Note that instead of the processor 110, the control unit may also be configured using a logic circuit such as an FPGA (Field Programmable Gate Array). The control unit may also be configured by combining the processor 110 with a logic circuit.

The storage unit is configured using a storage device consisting of, for example, memory 120 and persistent storage device 130, and stores a fault tree generation program that supplies various processing commands to the control unit, as well as data representing various information used in the processing executed by the control unit.

The memory unit has at least the following functional blocks: a mathematical formula database 310, a mathematical formula-based causal model database 320, a defect information database 330, a defect information-based causal model database 340, a variable database 350, a variable knowledge database 360, an expression format mathematical formula database 370, and a variable relation database 380.

The mathematical formula database 310 is a database for storing and managing mathematical formulas related to physical phenomena.

An example of the configuration of the formula database 310 is shown in Figure 2. The formula database 310 has a record for each formula ID, which is an identifier for uniquely identifying each formula. The record represents this formula ID, the name of the product, part, etc. (hereinafter referred to as "part, etc.") to which the formula represented by the formula ID applies, the variables on the left side of the formula, and the variables on the right side of the formula. In other words, the formula database 310 links and records, for each record, the formula ID, the application target of the formula represented by the formula ID, the variables on the left side, and the variables on the right side.

For example, in a part where the shaft is press-fitted into a cylindrical part to fasten it, the friction force F generated in the shaft can be calculated using the following (Equation 1):

In the example shown in Figure 2, the above (Equation 1) is assigned the formula ID "1" in the formula database 310. The record in formula database 310 corresponding to the above (Equation 1) also records that the formula applies to a "fastening part between a shaft and a cylindrical part (fastening part between a shaft and a cylinder)," that the variable on the left side of the formula is the "friction coefficient of the shaft part of the part (shaft: friction force)," and that the variables on the right side of the formula are the "friction coefficient between the cylindrical part and the shaft part of the part (between the cylinder and the shaft: friction coefficient), the internal pressure acting between the cylindrical part and the shaft part of the part (between the cylinder and the shaft: internal pressure), the area of the joint between the cylindrical part and the shaft part of the part (joint: area), and the inner diameter of the cylindrical part of the part (cylinder: inner diameter)."

As mentioned above, in the records of the formula database 310, the variables on the left and right sides are recorded as a set with the part to which the formula is applied, in a format such as "part: variable." For example, the "coefficient of friction between the cylindrical portion and the shaft portion" is recorded as "between the cylinder and the shaft: coefficient of friction." If there are multiple variables, they are stored separated by ",".

Furthermore, the internal pressure P between the cylindrical portion and the shaft portion can be calculated using the following (Equation 2).

In the example shown in Figure 2, the above (Equation 2) is assigned the formula ID "2" in the formula database 310. The record in formula database 310 corresponding to the above (Equation 2) also records that the formula applies to a "fastening part between a shaft and a cylindrical part (fastening part between a shaft and a cylinder)," that the variable on the left side of the formula is the "internal pressure acting between the cylindrical part and the shaft part of the part (internal pressure between the cylinder and the shaft)," and that the variables on the right side of the formula are the "inner diameter of the cylindrical part of the part (cylinder: inner diameter), outer diameter of the cylindrical part (cylinder: outer diameter), elastic modulus of the shaft part of the part (shaft: elastic modulus), and interference of the shaft part (shaft: interference)."

Furthermore, the area A of the joint between the cylindrical portion and the shaft portion of the part can be calculated using the following (Equation 3).

In the example shown in Figure 2, the above (Equation 3) is assigned the formula ID "3" in the formula database 310. Furthermore, the record in the formula database 310 corresponding to the above (Equation 3) records that the formula is applied to a "fastening part between a shaft portion and a cylindrical portion (fastening part between a shaft and a cylinder)," that the variable on the left side of the formula is the "area of the joint between the cylindrical portion and the shaft portion of the part (joint: area)," and that the variables on the right side of the formula are the "inner diameter of the cylindrical portion of the part (cylinder: inner diameter) and the insertion length of the shaft portion of the part (shaft: insertion length)."

The mathematical expression-based causal model database 320 is a database for storing and managing mathematical expression-based causal models generated by the mathematical expression-based causal model generation unit 220.

The defect information database 330 is a database for managing defect information.

The defect information-based causal model database 340 is a database for storing and managing defect information-based causal models generated by the defect information-based causal model generation unit 240.

The variable database 350 is a database for storing and managing variables extracted from technical documents.

An example of the configuration of the variable database 350 is shown in Figure 3. The variable database 350 has a record for each variable symbol, which is a symbol that represents a variable. The record represents this variable symbol and the phenomenon that is the target of the variable represented by that variable symbol (hereinafter referred to as the "target phenomenon"). In other words, the variable database 350 defines the target phenomenon of the variable represented by that variable symbol by linking and recording the variable symbol and the target phenomenon. In the example shown in Figure 3, the variable database 350 records that the target phenomenon of the variable represented by the variable symbol "T" is "torque."

The variable knowledge database 360 is a database for storing and managing variable knowledge, which is various types of knowledge related to variables.

An example of the configuration of the variable knowledge database 360 is shown in Figure 4. The variable knowledge database 360 has a record for each target product, facility, etc. (hereinafter collectively referred to as "product, etc."). The record represents the name of the target product, etc., the name of a part of the product, etc., the name of a portion of the part, a numerical value representing the size of the portion, and the unit of the numerical value. The numerical value is recorded as a numerical range by recording a minimum and maximum value. In other words, variable knowledge includes at least one of a physical quantity related to a physical phenomenon and a design value related to the design of the product, etc. The variable knowledge database 360 centrally manages variable knowledge by linking and recording the target product, etc., the part of the product, etc., the portion of the part, the numerical value representing the size of the portion, and the unit of the numerical value. In the example shown in Figure 4, the variable knowledge database 360 records that the "diameter" of the "shaft" of a "wind turbine" is "10 cm to 100 cm."

The expression format mathematical expression database 370 is a database for managing the expression format of each mathematical expression.

An example of the configuration of the representation format mathematical formula database 370 is shown in Figure 5. The representation format mathematical formula database 370 has a record for each mathematical formula ID. The record represents the mathematical formula ID and the representation format of the mathematical formula represented by that mathematical formula ID (hereinafter referred to as the "representation format mathematical formula"). In other words, the representation format mathematical formula database 370 centrally manages the representation formats of mathematical formulas by linking and recording the mathematical formula ID and the representation format of the mathematical formula represented by that mathematical formula ID. In the example shown in Figure 5, the representation format mathematical formula database 370 records that the above (Formula 1) represented by formula ID "1" is expressed in the format "T = \mu P A \frac{d_1}{2}".

The variable relationship database 380 is a database for storing and managing variable relationships that represent the relationship between the variables on the left side of each mathematical expression related to physical phenomena and the variables on the right side of that mathematical expression.

An example of the configuration of this variable relation database 380 is shown in Figure 6. The variable relation database 380 has a record for each formula ID. The record represents the formula ID, a variable symbol (hereinafter referred to as the "left-hand side variable symbol") representing the variable on the left side of the formula represented by the formula ID, the state of the variable on the left side of the formula (hereinafter referred to as the "left-hand side variable state"), the range of values that the variable on the right side of the formula can take when the variable on the left side is increased (hereinafter referred to as the "right-hand side variable range when the variable on the left side is increased"), the state of the variable on the right side of the formula in the left-hand side variable state (hereinafter referred to as the "right-hand side variable state"), and whether or not the left-hand side variable state in the formula is the target of a user selection operation of the fault tree generation system 100. If the left-hand side variable state in the formula is the target of a user selection operation of the fault tree generation system 100, a "1" is recorded in the record, and if it is not the target of a user selection operation, a "0" is recorded. That is, the variable relation database 380 centrally manages the relationship between the left-hand side variable and the right-hand side variable in a formula by linking and recording the formula ID, the left-hand side variable symbol, the left-hand side variable state, the right-hand side variable range when the left-hand side variable is increased, and the right-hand side variable state of the formula represented by the formula ID. According to the example shown in FIG. 6 , for the formula 1 represented by formula ID "1," the variable relation database 380 records that when the left-hand side variable represented by the symbol "\mu" is in the "increasing" state, the right-hand side variable is also in the "increasing" state, the range of values that the right-hand side variable can take in the formula when the left-hand side variable is increased is "(-∞ to +∞)," and the left-hand side variable state in the formula is the target of a user selection operation of the fault tree generation system 100.

The memory unit may also have a variable synonym dictionary database as a functional block.

An example of the configuration of this variable synonym dictionary database is shown in Figure 7. The variable synonym dictionary database has a record for each target phenomenon. The record represents a general name for the target phenomenon, synonyms for that name, and applications of that name and synonyms. In other words, the variable synonym dictionary database centrally manages the name of the target phenomenon and its synonyms by linking and recording the name of the target phenomenon, synonyms for that name, and applications of that name and synonyms. In the example shown in Figure 7, the target phenomenon represented by the name "torque" has a synonym "frictional force," and it is recorded that these terms are applied to "fastening."

The control unit can execute the various processes described above by reading and writing this information to the memory unit.

The user interface unit accepts input operations from the user and is responsible for user interface-related processing such as image display and audio output. The user interface unit has the functional blocks of an input unit and an output unit. The input unit detects various operations from the user. The input unit is configured using input devices 140 such as a keyboard, pointing device, or touch panel. The output unit displays the created production and logistics plan on a display device, and also displays various screens on the display device and outputs audio. The output unit is configured using output devices 150 such as an LCD display or touch screen.

The communication unit is responsible for communication processing with other devices, such as information terminals 20 owned by each user of the fault tree generation system 100, via the Internet (an example of the communication network 50). The communication unit is configured using a communication device 160, such as a NIC (Network Interface Card) or HBA (Host Bus Adapter).

In other words, each component of the fault tree generation system 100 is realized by hardware including storage devices such as the processor 110, memory 120, and persistent storage device 130, as well as wired or wireless communication lines and interface devices that connect them, and software stored in the storage devices (120, 130) that supplies processing instructions to the computing units.

In this embodiment, the functions of the fault tree generation system 100 have been described as being implemented integrally by a single computer device. However, these functions may also be implemented by multiple interconnected computer devices or server devices. Furthermore, the fault tree generation system 100 may be configured to include a general-purpose computer device such as a laptop PC and a web browser installed on it, or may be configured to include a web server and various mobile devices.

Also, the description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

<Causal model>
As described above, the mathematical-expression-based causal model generating section 220 generates a mathematical-expression-based causal model from the relationship between the variables on the left side and the variables on the right side of the mathematical expressions stored in the mathematical expression database 310 .

In addition, as described above, the defect information-based causal model generation unit 240 uses natural language processing to extract the causal relationships of defects contained in the defect information stored in the defect information database 330, and generates a defect information-based causal model.

Here, a causal model is a model of causal relationships.

Figure 8 shows an example of the configuration of a causal model.

Each element 801 that makes up the causal chain is made up of a part 802 and a phenomenon 803. These elements are expressed as a chain in the order of causality. In the example shown in Figure 8, the element on the right causes the element on the left.

The relationships connecting elements include AND condition 804 and OR condition 805. AND condition 804 indicates that the element to the right is triggered when all of the connected elements occur. On the other hand, OR condition 805 indicates that the element to the right is triggered when any one of the connected elements occurs.

The causal model illustrated in Figure 8 shows that phenomenon B807 occurs in part B806 when phenomenon D809 occurs in part D808, or when phenomenon E811 occurs in part E810.

The causal model illustrated in Figure 8 also shows that phenomenon B807 occurs in part B806, and phenomenon C813 occurs in part C812, which causes phenomenon A803 to occur in part A802.

A causal model generated from a mathematical formula in this way is called a mathematical formula-based causal model. On the other hand, a causal model generated from the causal relationships of defects described in defect information is called a defect information-based causal model.

<Mathematical formula-based causal model generation process>
Next, the mathematical expression-based causal model generation process executed by the fault tree generation system 100 according to this embodiment will be described with reference to FIGS.

Figure 9 is a flowchart 900 showing an example of the flow of the mathematical expression-based causal model generation process performed in this embodiment.

In step S901, the control unit of the fault tree generation system 100 uses the mathematical-based causal model generation unit 220 to determine that the inadequacy of the variable on the left side is a malfunction event, and further executes processing to generate elements of the large and small variables on the left side as its causes. For example, in the following (Equation 4), as shown in Figure 10, the string "inappropriate" is added to the variable "Part A: Variable A" on the left side, resulting in "Part A: Variable A inappropriate" 1001.

Part A: Variable A = f (Part B1: Variable B1, Part B2: Variable B2, ..., Part Bn: Variable Bn) ... (Equation 4)

In the above (Equation 4), each variable is expressed in the format "part: variable". For example, the left side of the above (Equation 1) is expressed in the format "shaft: frictional force". Variables have a range within which the product operates normally, and if this range is exceeded, the product will malfunction. Exceeding the normal range can occur when the variable becomes larger or smaller than the normal range. For this reason, the string "large" or "small" is added to "Part A: Variable A" as a cause of "Part A: Variable A unsuitable" 1001, generating the elements "Part A: Variable A large" 1002 and "Part A: Variable A small" 1003. After completing the processing in step S901, the control unit of the fault tree generation system 100 proceeds to step S902.

In step S902, the control unit of the fault tree generation system 100 executes a process in which the mathematical-based causal model generation unit 220 generates a model in which the large and small variables on the left side generated in step S901 are considered to be the causes of the variable on the right side becoming inappropriate, and the inappropriate variable on the right side is used as an element. For example, in the case of the above (Equation 4), as illustrated in Figure 11, "Component A: Large Variable A" 1002 and "Component A: Small Variable A" 1003 generated in step S901 are respectively added with "Component B1: Inappropriate Variable B1" 1104, "Component B2: Inappropriate Variable B2" 1105, ..., and "Component Bn: Inappropriate Variable Bn" 1106. Note that in the example shown in Figure 11, "Component B1: Variable B1 Unsuitable" 1104, "Component B2: Variable B2 Unsuitable" 1105, ..., "Component Bn: Variable Bn Unsuitable" 1106, which were added to "Component A: Variable A Small" 1003, are not shown. When the control unit of the fault tree generation system 100 completes the processing in step S902, it proceeds to step S903.

In step S903, the control unit of the fault tree generation system 100 executes a process in which the mathematical-based causal model generation unit 220 generates elements for the large and small variables on the right-hand side as factors contributing to the variable incompatibility on the right-hand side generated in step S902. As with step S901, this is based on the idea that variables have a range within which a product operates normally, and that exceeding this range will cause a product malfunction. Exceeding the normal range can mean that the variable is larger or smaller than the normal range. For example, in the case of the above (Equation 4), as illustrated in Figure 12, "Component B1: Large Variable B1" 1201 and "Component B1: Small Variable B1" 1202 are added to "Component B1: Variable B1 Incompatibility" 1104, "Component B2: Variable B2 Incompatibility" 1105, ..., and "Component Bn: Variable Bn Incompatibility" 1106 generated in step S902. Although not shown in FIG. 12, the same factors as those for "Component A: Variable A Large" are also added to "Component A: Variable A Small." This allows the fault tree generation system 100 to generate a mathematical-formula-based causal model. Upon completing the processing in step S903, the control unit of the fault tree generation system 100 terminates the mathematical-formula-based causal model generation processing shown in flowchart 900 of FIG. 9.

In this way, the mathematical-formula-based causal model generation unit 220 generates a mathematical-formula-based causal model from the relationship between the variables on the left side and the variables on the right side of the mathematical formula stored in the mathematical formula database 310, as described above, through steps S901 to S903. In other words, if there are variables on the left side and the right side, the mathematical-formula-based causal model generation unit 220 can generate a mathematical-formula-based causal model through steps S901 to S903. Therefore, the control unit of the fault tree generation system 100 generates a mathematical-formula-based causal model by using the mathematical-formula-based causal model generation unit 220 to obtain variables from the "Variables on the Left Side" and "Variables on the Right Side" columns of the table in the mathematical formula database 310 shown in FIG. 2. For example, when generating a causal model for the above (Equation 1), the left-hand side variable is "axis: friction force," and the right-hand side variables are "cylinder and axis: friction coefficient, cylinder and axis: internal pressure, joint: area, cylinder: inner diameter," and a mathematical formula-based causal model is generated.

Figure 13 shows an example of a mathematical-formula-based causal model generated by the mathematical-formula-based causal model generation unit 220 based on the above (Equation 1). Similarly, Figure 14 shows an example of a mathematical-formula-based causal model generated by the mathematical-formula-based causal model generation unit 220 based on the above (Equation 2), and Figure 15 shows an example of a mathematical-formula-based causal model generated by the mathematical-formula-based causal model generation unit 220 based on the above (Equation 3). The mathematical-formula-based causal model generated by the mathematical-formula-based causal model generation unit 220 is stored in the mathematical-formula-based causal model database 320.

<Formula-based fault tree generation process>
Next, the mathematical expression-based fault tree generation process executed by the fault tree generation system 100 according to this embodiment will be described.

First, the control unit of the fault tree generation system 100 executes processing in which the mathematical-based fault tree generation unit 230 extracts, from the mathematical-based causal model database 320, a mathematical-based causal model related to the top event received by the top event input unit 210. For example, when an input operation specifying "axis: inadequate friction force" as the top event is received from the user of the fault tree generation system 100, the mathematical-based causal model database 320 is searched and a mathematical-based causal model including "axis: inadequate friction force" is extracted from the mathematical-based causal model database 320. As a result, the mathematical-based causal model illustrated in Figure 6, which was generated based on the above-mentioned (Equation 1), is extracted as a search result.

Next, the control unit of the fault tree generation system 100 executes a process of searching the mathematical-formula-based causal model database 320 for mathematical-formula-based causal models that include the individual elements of the mathematical-formula-based causal model extracted in step S1601. For example, when a search is performed for "Between cylinder and shaft: large internal pressure," which is one of the elements of the mathematical-formula-based causal model generated based on (Equation 1) above, the mathematical-formula-based causal model illustrated in FIG. 7, which was generated based on (Equation 2) above, is extracted as a search result. Similarly, when a search is performed for "Joint: large area," which is one of the elements of the mathematical-formula-based causal model generated based on (Equation 1) above, the mathematical-formula-based causal model illustrated in FIG. 8, which was generated based on (Equation 3) above, is extracted as a search result.

The control unit of the fault tree generation system 100 then uses the mathematical-based fault tree generation unit 230 to combine these matched mathematical-based causal models with the mathematical-based causal model generated based on the above-mentioned (Equation 1). It then searches for each element of the combined mathematical-based causal model, and combines any matches. This process is repeated and generated, and this is called a mathematical-based fault tree. Figure 16 shows an example of a mathematical-based fault tree generated by the mathematical-based fault tree generation process. Figure 17 also shows the correspondence between the above-mentioned (Equation 1) and the mathematical-based fault tree generated based on the above-mentioned (Equation 1), as shown in Figure 16. In this way, the fault tree generation system 100 can generate fault trees for all top events without omissions or duplications.

The fault tree generation system 100 may calculate and display a score for each element of the generated fault tree. In such cases, the fault tree generation system 100 may gray out or hide elements with low scores. There are two ways to calculate the score:

The first method is to use a formula. As mentioned above, when a formula-based causal model is generated based on the above (Formula 1), an example is shown in Figure 6. Two factors are ultimately generated as factors contributing to "shaft: large friction coefficient", namely "cylinder and shaft: large friction coefficient" and "cylinder and shaft: small friction coefficient". In the above (Formula 1), when the variable on the right-hand side, "cylinder and shaft: friction coefficient", increases, the variable on the left-hand side, "shaft: friction coefficient", increases. Conversely, when the variable on the right-hand side, "cylinder and shaft: friction coefficient", decreases, the variable on the left-hand side, "shaft: friction coefficient", decreases. In other words, "cylinder and shaft: large friction coefficient" is a more appropriate factor contributing to "shaft: large friction coefficient", and "cylinder and shaft: large friction coefficient" has a higher score. In this way, the score is calculated from the effect on the left-hand side when the variable on the right-hand side is changed.

The second method is to use past performance, that is, past defect information. Specifically, this method calculates the score based on the number of defect information items in which a defect event in the fault tree and an event that is a candidate for its cause are both listed. For example, the defect information database 330 is searched to find the number of defect information items in which both "shaft: large friction coefficient" and "cylinder and shaft: large friction coefficient" are listed, and the number of defect information items in which both "shaft: large friction coefficient" and "cylinder and shaft: small friction coefficient" are listed. If there are a large number of defect information items in which both "shaft: large friction coefficient" and "cylinder and shaft: large friction coefficient" are listed, this means that "cylinder and shaft: large friction coefficient" is more likely and valid as a cause of "shaft: large friction coefficient".

<Outline of the Formula Decoding Unit and Formula Processing Unit>
The fault tree generation system 100 according to this embodiment can appropriately reduce the branches of the generated fault tree as illustrated in Fig. 18 by the processes executed by the mathematical formula interpretation unit 260 and the mathematical formula processing unit 270, respectively. As a result, the fault tree generation system 100 can generate a fault tree that extracts all possible causes of failures and other issues without omissions or duplications, and that enables the user of the fault tree generation system 100 to efficiently investigate the cause of a malfunction or perform design-time examinations. Fig. 18 shows how the branches of the fault tree illustrated in Fig. 17 are appropriately reduced as a result of the processes executed by the mathematical formula interpretation unit 260 and the mathematical formula processing unit 270. Next, an overview of the mathematical formula interpretation unit 260 and the mathematical formula processing unit 270 will be described below with reference to Figs. 19 to 24.

Figure 19 is a diagram showing an example of the mathematical formula decoding unit 260 and the mathematical formula processing unit 270.

As shown in FIG. 19, the fault tree generation system 100 appropriately reduces the branches of the generated fault tree by sequentially executing the variable condition generation process performed by the formula interpretation unit 260 (described in detail below in relation to FIG. 20) and the process performed by the formula processing unit 270 (described in detail below in relation to FIG. 21). In this process, the formula interpretation unit 260 executes the variable condition generation process using the target device, component, phenomenon, and formula ID entered by the user of the fault tree generation system 100, and the variables selected, as input data, as shown in FIG. 19. In addition, the fault tree generation system 100 allows the user to enter a formula by specifying a formula ID and reading the corresponding formula from the formula database 310, as shown in FIG. 19. It is also possible to directly enter the formula into a specified input field. Furthermore, when the fault tree generation system 100 directly accepts input of a mathematical formula, the notation of the mathematical formula may be a representation-style formula, as shown in FIG. 19, a simulation-style formula, or even a formula expressed in another style.

(Variable Condition Generation Process of the Formula Decoding Unit 260)
FIG. 20 is a flowchart 2000 showing an example of the flow of the variable condition generation process executed by the mathematical expression decoding unit 260.

In step S2001, the control unit of the fault tree generation system 100 executes a process in which the formula interpretation unit 260 compares the characters of the formula entered by the user with the variable data stored in the variable database 350, starting from the beginning, and creates a variable list for the formula. When the control unit of the fault tree generation system 100 completes the process in step S2001, it proceeds to step S2002.

In step S2002, the control unit of the fault tree generation system 100 causes the formula interpretation unit 260 to search the variable knowledge database 360 for the variables included in the variable list generated in step S2001, obtain the numerical values and units of the records that correspond to the conditions related to the target devices, etc. and components input by the user of the fault tree generation system 100, and executes processing to create a numerical value list. When the control unit of the fault tree generation system 100 completes the processing in step S2002, it proceeds to step S2003.

In step S2003, the control unit of the fault tree generation system 100 executes a process in which the formula interpretation unit 260 calculates a list of multiples that aligns the powers of the same type of unit (length, weight, etc.) for the numerical value list created in step S2002. After completing the process in step S2003, the control unit of the fault tree generation system 100 proceeds to step S2004.

In step S2004, the control unit of the fault tree generation system 100 causes the formula interpretation unit 260 to execute a process of multiplying the values in the number list created in step S2002 by the values in the multiple list calculated in step S2003. After completing the process in step S2004, the control unit of the fault tree generation system 100 proceeds to step S2005.

In step S2005, the control unit of the fault tree generation system 100 causes the formula interpretation unit 260 to execute a process of outputting the minimum, median, and maximum values of the numerical range for all variables in the formula as variable conditions. This generates variable conditions for the formula. Upon completing the process in step S2005, the control unit of the fault tree generation system 100 terminates the variable condition generation process shown in flowchart 2000 of FIG. 20.

(Mathematical formula processing unit 270)
FIG. 21 is a flowchart 2100 showing an example of the flow of processing executed by the mathematical-formula processing unit 270.

In step S2101, the control unit of the fault tree generation system 100 executes processing to select one variable candidate using the mathematical processing unit 270 in response to a user selection operation received via the input unit or selection unit. The fault tree generation system 100 displays the setting user interface (2200, 2300) of the mathematical processing unit 270 illustrated in Figures 22 and 23 on the display device of the information terminal 20 held by the user or on the output device 150 of the fault tree generation system 100, and receives the user's selection operation and/or input operation. When the control unit of the fault tree generation system 100 completes the processing in step S2101, it proceeds to step S2102.

In step S2102, the control unit of the fault tree generation system 100 executes processing to create a list of combinations of variable conditions for other variables using the mathematical processing unit 270. When the processing in step S2102 is completed, the control unit of the fault tree generation system 100 proceeds to step S2103.

In step S2103, the control unit of the fault tree generation system 100 executes a process in which the mathematical processing unit 270 selects one record from the combination list created in step S2102. The control unit of the fault tree generation system 100 repeatedly executes the processes of steps S2103 to S2107 for each record in the combination list. When the control unit of the fault tree generation system 100 completes the process in step S2103, it proceeds to step S2104.

In step S2104, the control unit of the fault tree generation system 100 uses the mathematical processing unit 270 to calculate an equation in which the variable condition of the variable on the left side of the equation is applied to the equation. For example, when the variable on the left side of the equation is ≧0, the calculation is performed assuming that the right side of the equation is ≧0. Then, for the results obtained by this calculation process, processing is performed to obtain extreme value conditions and new variable conditions. When the processing in step S2104 is completed, the control unit of the fault tree generation system 100 proceeds to step S2105.

In step S2105, the control unit of the fault tree generation system 100 executes processing by the mathematical processing unit 270 to generate a variable condition that combines the variable condition and the previously obtained variable condition. When the processing in step S2105 is completed, the control unit of the fault tree generation system 100 proceeds to step S2106.

In step S2106, the control unit of the fault tree generation system 100 causes the mathematical processing unit 270 to differentiate the mathematical expression and perform a simulation. Specifically, the mathematical expression is differentiated, the conditions under which the differentiated function becomes 0 are calculated, the presence or absence of extreme values is checked, and a simulation is performed using the variable conditions. When the control unit of the fault tree generation system 100 completes the processing in step S2106, it proceeds to step S2107.

In step S2107, the control unit of the fault tree generation system 100 performs a process in which the mathematical processing unit 270 determines whether the results of the simulation performed in step S2106 are positive or negative, and records the results in the increase/decrease relationship list.

The control unit of the fault tree generation system 100 repeatedly executes the processes of steps S2103 to S2107 for each record in the combination list. After executing the processes of steps S2103 to S2107 for all records in the combination list and completing the process of step S2107, the process returns to step S2101 and repeatedly executes the processes of steps S2101 to S2107 for all variable candidates. In other words, the control unit of the fault tree generation system 100 repeats the processes of steps S2101 to S2107 until they have been executed for all variables on the right-hand side of the equation. After executing the processes of steps S2101 to S2107 for all variable candidates and completing the process of step S2107, the control unit of the fault tree generation system 100 ends the process shown in flowchart 2100 of FIG. 21.

As a result, the fault tree generation system 100 can appropriately reduce the branches of the generated fault tree, as shown in the example in Figure 24. In this way, the fault tree generation system 100 generates a fault tree in which all possible causes of failures, etc. are extracted without omission or duplication, and then appropriately reduces the branches of the fault tree and presents it to the user. At this time, the fault tree may be presented to the user from the beginning in a state where the branches have been appropriately reduced, or the fault tree may be presented to the user in a form that allows comparison between the states before and after the branches have been appropriately reduced.

<Method of constructing the variable knowledge database 360>
Next, a method for constructing the variable knowledge database 360 described above in relation to FIG. 4 will be described with reference to FIG.

Figure 25 shows an example of how to build the variable knowledge database 360.

As shown in Figure 25, the control unit of the fault tree generation system 100 uses well-known natural language processing techniques to extract various data stored in the variable knowledge database 360, such as the name of the target product, the names of the parts of the product, the names of the parts, numerical values representing the size of the parts, and the units of the numerical values, from various technical documents such as design documents of the target product, CAD data, catalogs, past analysis reports, standards such as JIS, catalogs of the target product, and papers.

The embodiments of the present invention described above can be summarized as follows:

(1) The fault tree generation system 100 is a system for generating a fault tree and includes at least a processor 110 and memory devices (120, 130). The memory devices (120, 130) include at least a mathematical formula database 310 that stores mathematical formulas related to physical phenomena, a variable database 350 that stores variables extracted from technical documents, a variable knowledge database 360 that stores variable knowledge, which is knowledge related to variables, and a variable relationship database 380 that stores variable relationships that represent the relationship between variables on the left side of mathematical formulas related to the physical phenomena and variables on the right side of the mathematical formulas. The processor 110 develops the causal relationships of malfunction events based on the mathematical expressions stored in the mathematical expression database 310, and generates a mathematical expression-based causal model that is a model representing the causal relationships; based on the generated mathematical expression-based causal model, generates and outputs a fault tree that combines causal relationships related to any malfunction event from the developed causal relationships of malfunction events; based on the variable database 350 and the variable knowledge database 360, generates a variable list of the mathematical expression related to the physical phenomenon, the variables, and variable conditions that are possible numerical values for the variables; extracts variable relationships between variables on the left side and variables on the right side of the mathematical expression from the mathematical expression related to the physical phenomenon and the generated variable list and variable conditions, and stores the variable relationships in the variable relationship database 380; and when generating the fault tree, constructs causal relationships related to the malfunction event based on the variable relationships stored in the variable relationship database 380, thereby reducing the number of branches in the fault tree. In this way, the fault tree generation system 100 can generate and output a fault tree in which all candidate causes of failures, etc. are extracted without omission or duplication, while narrowing down the extracted fault tree to an appropriate number. As a result, users of the fault tree generation system 100 can perform fault tree analysis using a fault tree in which all candidate causes of failures, etc. are extracted without omission or duplication, while the number of branches is appropriately reduced, thereby enabling efficient cause investigation when a malfunction occurs and review work during design.

(2) In the output fault tree, both the resulting higher-level events and the causal lower-level events are expressed as physical quantities, and the relationship between the physical quantities of the higher-level events and the lower-level events is expressed using mathematical formulas.

(3) The processor 110 combines the generated fault tree with the causal relationships extracted from the malfunction information.

(4) Variable knowledge includes at least one of physical quantities related to physical phenomena and design values related to the design of products, etc.

(5) When generating a mathematical formula-based causal model, the processor 110 determines the causes of the inappropriateness of the variable on the left side of the mathematical formula as being when the variable on the left side is large and when the variable on the left side is small, and develops the causal relationship of the defect according to the variable relationship between the variable on the left side of the mathematical formula and the variable on the right side of the mathematical formula stored in the variable relationship database 380.

(6) When generating a mathematical formula-based causal model, the processor 110 determines the causes of the inappropriateness of the variable on the right side of the mathematical formula as being when the variable on the right side is large and when the variable on the right side is small, and develops the causal relationship of the defect according to the variable relationship between the variable on the left side of the mathematical formula and the variable on the right side of the mathematical formula stored in the variable relationship database 380.

(7) When constructing a causal relationship related to the malfunction event, the processor 110 accepts a selection operation or input operation to specify a variable candidate.

(8) The processor 110 accepts input operations to specify variable conditions when constructing causal relationships related to the malfunction event.

Note that the present invention is not limited to the above-described embodiment, and can be implemented using any components within the scope of the present invention.

The above-described embodiments, examples, and modifications are merely examples, and the present invention is not limited to these details as long as the characteristics of the invention are not impaired. Furthermore, while various embodiments, examples, and modifications have been described above, the present invention is not limited to these details. Other aspects that can be considered within the technical scope of the present invention are also included within the scope of the present invention.

In the above diagrams, the control and information lines shown are those considered necessary for explanation, and do not necessarily represent all control and information lines in the actual implementation. For example, it is safe to assume that in reality, almost all components are interconnected.

Furthermore, the layout of the various functional units, processing units, and databases of the fault tree generation system 100 described above is merely an example. The layout of the various functional units, processing units, and databases can be changed to an optimal layout in terms of the performance, processing efficiency, communication efficiency, etc. of the hardware and software provided in the fault tree generation system 100.

100: Fault tree generation system

Claims (8)

1. A fault tree generation system for generating a fault tree, comprising:
comprising at least a processor and a storage device;
The storage device includes:
a mathematical formula database storing mathematical formulas relating to physical phenomena;
a variable database for storing variables extracted from technical documents;
a variable knowledge database that stores variable knowledge, which is knowledge about variables;
a variable relation database that stores variable relations that represent the relationship between variables on the left side of a mathematical expression related to the physical phenomenon and variables on the right side of the mathematical expression;
The processor:
An inadequacy of a variable on the left side of a mathematical formula stored in the mathematical formula database is regarded as a malfunction event, and elements of large and small variables of the variable on the left side are generated as factors of the malfunction event, and the factors of large and small variables of the variable on the left side generated are considered to be factors when a variable on the right side of a mathematical formula stored in the mathematical formula database becomes inappropriate, and the inadequacy of the variable on the right side is generated as an element, and elements of large and small variables of the variable on the right side are generated as factors of the inadequacy of the variable on the right side generated, thereby generating a mathematical formula-based causal model which is a model representing the causal relationship of the malfunction event from the relationship between the variable on the left side and the variable on the right side of the mathematical formula stored in the mathematical formula database,
generating and outputting a fault tree that combines causal relationships related to any malfunction event from among the causal relationships of the malfunction events based on the generated mathematical formula-based causal model;
generating a list of variables for a mathematical formula relating to the physical phenomenon, the variables, and variable conditions that are the values that the variables can take, based on the variable database and the variable knowledge database;
extracting variable relationships between variables on the left side and variables on the right side of the mathematical formula from the mathematical formula related to the physical phenomenon and the generated variable list and variable conditions, and storing the extracted variable relationships in the variable relationship database;
A fault tree generation system that uses the generated variable conditions to differentiate and simulate the variables on the left and right sides of an equation related to the physical phenomenon, determines the increase/decrease relationship of each variable with respect to the malfunction event based on the results of the simulation, and eliminates causal relationships included in the fault tree that do not affect the malfunction event based on the determined increase/decrease relationship, thereby reducing the number of branches in the fault tree. the storage device further includes a defect information database for storing defect information;
the processor combines, with the generated fault tree, causal relationships extracted from failure information representing information about the failure event ;
The fault tree generation system of claim 1 . 2. The fault tree generation system according to claim 1, wherein the variable knowledge includes at least one of a physical quantity related to a physical phenomenon and a design value related to the design of a product or facility . When generating the mathematical-formula-based causal model, the processor determines that the cause of the inappropriateness of a variable on the left side of the mathematical formula is a case where the variable on the left side is large and a case where the variable on the left side is small.
The fault tree generation system of claim 1 . When generating the mathematical-formula-based causal model, the processor determines that the cause of the inappropriateness of the variable on the right side of the mathematical formula is a case where the variable on the right side is large and a case where the variable on the right side is small.
The fault tree generation system of claim 1 . The fault tree generation system of claim 1, wherein the processor accepts a selection or input operation for specifying candidate variables when constructing causal relationships related to the malfunction event. The fault tree generation system of claim 1, wherein the processor accepts an input operation for specifying variable conditions when constructing causal relationships related to the malfunction event. 1. A fault tree generation method for generating a fault tree, comprising:
a computer having at least a processor and a storage device,
The system has at least a mathematical formula database that stores mathematical formulas related to physical phenomena, a variable database that stores variables extracted from technical documents, a variable knowledge database that stores variable knowledge that is knowledge related to variables, and a variable relation database that stores variable relations that represent the relationship between variables on the left side of mathematical formulas related to the physical phenomena and variables on the right side of the mathematical formulas,
An inadequacy of a variable on the left side of a mathematical formula stored in the mathematical formula database is regarded as a malfunction event, and elements of large and small variables of the variable on the left side are generated as factors of the malfunction event, and the factors of large and small variables of the variable on the left side generated are considered to be factors when a variable on the right side of a mathematical formula stored in the mathematical formula database becomes inappropriate, and the inadequacy of the variable on the right side is generated as an element, and elements of large and small variables of the variable on the right side are generated as factors of the inadequacy of the variable on the right side generated, thereby generating a mathematical formula-based causal model which is a model representing the causal relationship of the malfunction event from the relationship between the variable on the left side and the variable on the right side of the mathematical formula stored in the mathematical formula database,
generating and outputting a fault tree that combines causal relationships related to any malfunction event from among the causal relationships of the malfunction events based on the generated mathematical formula-based causal model;
generating a list of variables for a mathematical formula relating to the physical phenomenon, the variables, and variable conditions that are the values that the variables can take, based on the variable database and the variable knowledge database;
extracting variable relationships between variables on the left side and variables on the right side of the mathematical formula from the mathematical formula related to the physical phenomenon and the generated variable list and variable conditions, and storing the extracted variable relationships in the variable relationship database;
A fault tree generation method comprising: using the generated variable conditions, differentiating and simulating the variables on the left and right sides of an equation relating to the physical phenomenon; determining an increase/decrease relationship of each variable with respect to the malfunction event based on the results of the simulation; and excluding causal relationships included in the fault tree that do not affect the malfunction event based on the determined increase/decrease relationship, thereby reducing the number of branches in the fault tree.