JP7067971B2 - Processing section determination device, processing section determination method, program, and diagnostic device - Google Patents

Description

The present invention relates to a processing section determination device, a processing section determination method, a program, and a diagnostic device.

As a method for predicting and estimating tool abnormalities and machining quality when a machine tool or the like processes a workpiece, a method of detecting and using physical quantities such as current value information, vibration, or force of the machine motor has already been used. Are known.

As a device that detects such physical quantities and determines tool abnormalities, etc., a device that synchronizes the time-series data of machine information such as the load on the spindle with the event data of the device and detects abnormalities in machining operations. Is known (Patent Document 1).

However, with the technique described in Patent Document 1, it is difficult to clearly grasp the section of the actual processing process from the event data of the apparatus.

The present invention has been made in view of the above-mentioned problems, and provides a processing section determination device, a processing section determination method, a program, and a diagnostic device capable of accurately grasping an actual processing section. The purpose.

In order to solve the above-mentioned problems and achieve the object, the present invention has a first acquisition unit that acquires detection information output from a detection unit that detects a physical quantity that changes according to the operation of the target device, and the above-mentioned first acquisition unit. A second acquisition unit that acquires a signal indicating a section for operating the target device from the target device, and a first identification that distinguishes between a non-processed section and a processed section in the target device based on the detection information and the signal. A unit and a second identification unit that identifies a non-processed section and a processed section in the target device based on the detection information and the identification result by the first identification unit are provided.

According to the present invention, it is possible to accurately grasp the actual processing section.

FIG. 1 is a diagram showing an example of the overall configuration of the diagnostic system according to the embodiment as a functional block. FIG. 2 is a diagram showing an example of the hardware configuration of the processing machine according to the embodiment. FIG. 3 is a diagram showing an example of the hardware configuration of the diagnostic device according to the embodiment. FIG. 4 is a diagram showing an example of detection information and a ladder signal of the processing machine according to the embodiment. FIG. 5 is a diagram illustrating feature information extracted from detection information by the diagnostic apparatus according to the embodiment in terms of frequency components. FIG. 6 is a diagram illustrating an example in which the diagnostic apparatus according to the embodiment selects the feature information of the non-processed section and the processed section. FIG. 7 is a diagram illustrating the feature information selected from the non-processed section and the processed section by the diagnostic apparatus according to the embodiment in a frequency spectrum. FIG. 8 is a diagram showing an example in which the diagnostic apparatus according to the embodiment calculates the Euclidean distance to be a machined section. FIG. 9 is a diagram illustrating an operation of estimating a machining section from the machining section likeness calculated by the diagnostic apparatus according to the embodiment. FIG. 10 is a flow chart showing an example of a procedure of abnormality determination processing at the initial stage of processing by the diagnostic apparatus according to the embodiment. FIG. 11 is a flow chart showing an example of a procedure for estimating the processing section by the first identification unit according to the embodiment. FIG. 12 is a flow chart showing an example of a procedure for abnormality determination processing after a lapse of a predetermined period by the diagnostic apparatus according to the embodiment. FIG. 13 is a flow chart showing an example of a procedure for estimating the processing section by the second identification unit according to the embodiment. FIG. 14 is a schematic diagram illustrating an example in which processing in the same process is continuously performed a plurality of times in the processing machine according to the embodiment.

Hereinafter, embodiments of the processing section determination device, the processing section determination method, the program, and the diagnostic device according to the present invention will be described in detail with reference to FIGS. 1 to 14. Further, the present invention is not limited to the following embodiments, and the components in the following embodiments include those easily conceived by those skilled in the art, substantially the same, and so-called equivalent ranges. Is included. Further, various omissions, substitutions, changes and combinations of components can be made without departing from the gist of the following embodiments.

(Overall configuration of diagnostic system)
FIG. 1 is a diagram showing an example of the overall configuration of the diagnostic system 1 according to the embodiment as a functional block. The diagnostic device 100 as the processing section determination device included in the diagnostic system 1 is the first acquisition to acquire the detection information output from the detection unit 225 that detects the physical quantity that changes according to the operation of the processing machine 200 as the target device. The detection information receiving unit 112 as a unit, the processing information acquisition unit 101 as a second acquisition unit that acquires a ladder signal as a signal indicating a section in which the processing machine 200 is operated from the processing machine 200, and the detection information and the signal. Based on the first identification unit 104 that identifies the non-processed section and the processed section in the processing machine 200, the non-processed section and the processing in the processing machine 200 based on the detection information, the signal, and the identification result by the first identification unit. A second identification unit 105 for identifying a section is provided. The details of the diagnostic system 1 of the embodiment will be described below.

As shown in FIG. 1, the diagnostic system 1 of the embodiment includes a processing machine 200 and a diagnostic device 100 connected to the processing machine 200. The processing machine 200 is a machine tool that performs processing such as cutting, grinding, or polishing on a processing target using a tool. The processing machine 200 is an example of a target device to be diagnosed by the diagnostic device 100. The diagnostic device 100 is a device that is connected to the processing machine 200 so as to be communicable and diagnoses an abnormality in the operation of the processing machine 200.

The processing machine 200 includes a numerical control unit 201, a communication control unit 221, a drive control unit 223, a drive unit 224, and a detection unit 225.

The numerical control unit 201 is a functional unit that executes machining by the drive unit 224 by numerical control (NC: Numerical Control). For example, the numerical control unit 201 generates and outputs numerical control data that controls the operation of the drive unit 224. Further, the numerical control unit 201 has, as context information (machining information) indicating the operation state of the drive unit 224 that drives the tool, for example, a section (tool) from the feed operation to the machining target of the tool to the end of the actual machining process. A ladder signal, which is an ON / OFF signal indicating the feed section), is output to the communication control unit 221. The context information is a plurality of information defined for each type of operation of the processing machine 200. In addition to the above ladder signal, the context information includes, for example, identification information of the processing machine 200, identification information of the drive unit 224 (for example, tool identification information, etc.), the diameter of the tool driven by the drive unit 224, and the tool. Configuration information such as materials, operating state of the drive unit 224, cumulative usage time from the start of use of the drive unit 224, load applied to the drive unit 224, rotation speed of the drive unit 224, processing speed of the drive unit 224, etc. It may include information indicating information on processing conditions and the like.

For example, the numerical control unit 201 sequentially transmits the context information corresponding to the operation of the current processing machine 200 to the diagnostic apparatus 100 via the communication control unit 221. When machining an object to be machined, the numerical control unit 201 changes the type of the drive unit 224 to be driven or the drive state (rotational speed, rotation speed, etc.) of the drive unit 224 according to the processing process. Each time the type of operation is changed, the numerical control unit 201 sequentially transmits the context information corresponding to the changed type of operation to the diagnostic apparatus 100 via the communication control unit 221.

The communication control unit 221 is a functional unit that controls communication with an external device such as the diagnostic device 100. The communication control unit 221 transmits, for example, the context information corresponding to the current operation to the diagnostic apparatus 100.

The drive control unit 223 is a functional unit that drives and controls the drive unit 224 based on the numerical control data obtained by the numerical control unit 201.

The drive unit 224 is a functional unit that is the target of drive control by the drive control unit 223. The drive unit 224 drives the tool under the control of the drive control unit 223. The drive unit 224 is an actuator (motor) or the like that is driven and controlled by the drive control unit 223. The drive unit 224 may be any actuator as long as it is used for processing and is subject to numerical control. Further, two or more drive units 224 may be provided.

The detection unit 225 is a functional unit that detects a physical quantity generated by the processing machine 200 and outputs the detected physical quantity information to the diagnostic apparatus 100 as detection information (sensor data). The physical quantity generated by the processing machine 200 is vibration or sound generated by the processing machine 200. Such vibration or noise is generated, for example, when the tool installed in the processing machine 200 and the processing target come into contact with each other during the processing operation. Alternatively, such vibration or sound is generated by the tool or the processing machine 200 itself. The number of detection units 225 is arbitrary. For example, a plurality of detection units 225 for detecting the same physical quantity may be provided, or a plurality of detection units 225 for detecting different physical quantities may be provided. For example, when the blade, which is a tool used for machining, breaks or chipping of the blade occurs, the vibration or sound during machining changes. Therefore, by detecting the vibration data and the acoustic data by the detection unit 225 and making a judgment using a model or the like for determining normal vibration or sound, it is possible to detect an abnormality in the operation of the processing machine 200.

The diagnostic device 100 includes a communication control unit 111, a detection information receiving unit 112, a processing information acquisition unit 101, a feature extraction unit 102, a generation unit 103, a first identification unit 104, and a second identification unit 105. It has an abnormality determination unit 106, a storage unit 113, an input unit 114, a display control unit 107, and a display unit 115.

The communication control unit 111 is a functional unit that controls communication with the processing machine 200. For example, the communication control unit 111 receives context information from the numerical control unit 201 of the processing machine 200 via the communication control unit 221.

The detection information receiving unit 112 as the first acquisition unit is a functional unit that receives detection information from the detection unit 225 installed in the processing machine 200.

The processing information acquisition unit 101 as the second acquisition unit is a functional unit that acquires context information (machining information) received by the communication control unit 111 from the processing machine 200.

The feature extraction unit 102 as an extraction unit is a functional unit that extracts feature information used in various determinations from the detection information. For example, the feature information is used to discriminate between the non-machining section and the machining section from the section in which the ladder signal included in the context information is ON, that is, the tool feed section. Further, the feature information acquired when the processing machine 200 is normal is used when a model is generated by the generation unit 103 described later. Further, the feature information in the processing section acquired for performing the abnormality determination of the processing machine 200 is compared with the model generated by the generation unit 103 and used for the abnormality determination.

The generation unit 103 is a functional unit that generates models used for various determinations from the above-mentioned feature information. For example, a model generated from the feature information of the non-processed section is used to distinguish between the non-processed section and the processed section. Further, the model generated from the feature information of the machining section acquired when the machining machine 200 is normal is used for determining that the machining has been performed normally.

The first identification unit 104 is a functional unit that identifies a processing section from a non-processing section at the initial stage of processing by the processing machine 200. It cannot be said that there is sufficient data for identifying the machining section from the non-machining section such as the feature information and the model generated from the feature information for a certain period after the start-up of the machining machine 200 and the tool change. The first identification unit 104 estimates the processing section using a small amount of data. In order to realize such a function, the first identification unit 104 includes a selection unit 104a, a calculation unit 104b, and a section determination unit 104c.

The selection unit 104a is a functional unit that selects feature information for each predetermined frame from the feature information extracted by the feature extraction unit 102. The selection unit 104a selects the feature information of the non-machining section in the tool feed section. The selection unit 104a also selects the feature information of a section of the tool feed section that may be a machining section as the target feature information in order to compare with the feature information of the non-machining section. A model is generated by the generation unit 103 from the feature information of the selected non-processed section. Such a model is used for identification of a machined section as a model of a non-machined section as a first model.

The calculation unit 104b has a function of comparing the model of the non-processed section generated from the feature information of the non-processed section selected by the selection unit 104a with the target feature information and calculating the processing section-likeness of each target feature information. It is a department. As a comparison between the model and the target feature information, for example, a method of finding outliers of the target feature information from the model is used.

The section determination unit 104c is a functional unit that determines a threshold value for the processing section likeness calculated by the calculation unit 104b. The feature information of the section determined to be the machining section is determined as the feature information of the machining section. From this feature information, for example, a model is generated by the generation unit 103. Such a model is used for abnormality determination as a model of a machining section.

The second identification unit 105 is a functional unit that discriminates between the non-machined section and the machined section after sufficient data for discriminating between the non-machined section and the machined section is obtained. In order to realize such a function, the second identification unit 105 includes a selection unit 105a, a calculation unit 105b, a section determination unit 105c, and a feature information collection unit 105d.

The feature information collecting unit 105d collects the feature information of the non-processed section and the processed section extracted by the feature extraction unit 102 at the initial stage of processing in the processing machine 200. After sufficient feature information has been collected, the generation unit 103 generates a model of the non-machined section as the second model and a model of the machined section as the third model, respectively, from these feature information. These models are used to identify non-machined and machined sections.

The selection unit 105a is a functional unit that selects the feature information for each predetermined frame from the feature information extracted by the feature extraction unit 102. The selection unit 105a selects the feature information of the non-machining section in the tool feed section. The selection unit 105a also selects the feature information of the section of the tool feed section that may be the machining section as the target feature information in order to compare with the feature information of the non-machining section.

The calculation unit 105b is a functional unit that compares the model generated from the feature information collected by the feature information collection unit 105d with the target feature information and calculates the processing section-likeness of each target feature information. As a comparison between the model generated from the feature information of the unprocessed section and the target feature information, for example, a method of obtaining outliers of the target feature information from the model is used. As a comparison between the model generated from the feature information of the processed section and the target feature information, for example, a method of obtaining an approximate value with the model of the target feature information is used.

The section determination unit 105c is a functional unit that determines a threshold value for the processing section likeness calculated by the calculation unit 105b. The feature information of the section determined to be the machining section is determined as the feature information of the machining section.

The abnormality determination unit 106 uses the feature information extracted by the feature extraction unit 102 to be the target of the abnormality determination and the model for each processing section generated by the generation unit 103, and the operation of the processing machine 200 is normal. It is a functional unit that determines whether or not there is.

The storage unit 113 is a functional unit that stores the model generated by the generation unit 103 in association with the context information. Further, the storage unit 113 stores the detection information acquired by the detection information receiving unit 112 in association with the context information.

The input unit 114 is a functional unit that performs operations such as inputting characters and numbers, selecting various instructions, and moving a cursor.

The display control unit 107 is a functional unit that controls the display operation of the display unit 115. Specifically, the display control unit 107 causes the display unit 115 to display, for example, the result of the abnormality determination by the abnormality determination unit 106. The display unit 115 is a functional unit that displays various information according to the control by the display control unit 107.

The functional units of the diagnostic device 100 and the processing machine 200 conceptually show the functions, and are not limited to such a configuration. For example, a plurality of functional units shown as independent functional units in FIG. 1 may be configured as one functional unit. On the other hand, the function of one functional unit in FIG. 1 may be divided into a plurality of functions and configured as a plurality of functional units.

Further, the processing machine 200 and the diagnostic device 100 may be connected in any connection form. For example, the processing machine 200 and the diagnostic device 100 may be connected by a dedicated connection line, a wired network such as a wired LAN (Local Area Network), a wireless network, or the like.

Further, FIG. 1 shows an example in which one processing machine 200 is connected to the diagnostic device 100, but the present invention is not limited to this, and a plurality of processing machines 200 are connected to the diagnostic device 100. They may be connected so that they can communicate with each other.

(Hardware configuration of processing machine)
Next, a hardware configuration example of the processing machine 200 of the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing an example of the hardware configuration of the processing machine 200 according to the embodiment.

As shown in FIG. 2, the processing machine 200 is driven by a CPU (Central Processing Unit) 20, a ROM (Read Only Memory) 20a, a RAM (Random Access Memory) 20b, and a communication I / F (interface) 21. The control circuit 23 and the control circuit 23 are connected to each other so as to be communicable by the bus 2B.

The CPU 20 is an arithmetic unit that controls the entire processing machine 200. The CPU 20 controls the operation of the entire processing machine 200 and realizes a processing function, for example, by executing a program stored in the ROM 20a or the like with the RAM 20b as a work area (work area). The numerical control unit 201 of FIG. 1 is realized by, for example, a program running on the CPU 20.

The communication I / F 21 is an interface used for communication with an external device such as the diagnostic device 100. The communication I / F 21 is, for example, a NIC (Network Interface Card) corresponding to TCP (Transmission Protocol) / IP (Internet Protocol). The communication control unit 221 of FIG. 1 is realized by, for example, a communication I / F21 and a program operated by the CPU 20.

The drive control circuit 23 is a circuit that controls the drive of the motor 24. The motor 24 drives the tool 24a used for machining. The tool 24a includes a drill, an end mill, a tool tip, a grindstone, etc., and a table on which a machining object is placed and moved according to the machining. The drive control unit 223 of FIG. 1 is realized by, for example, a drive control circuit 23. The drive unit 224 of FIG. 1 is realized by, for example, a motor 24.

The sensor 25 is composed of, for example, a microphone device, a vibration sensor, an acceleration sensor, an AE (Acoustic Emission) sensor, or the like, and is installed in the vicinity of a tool capable of detecting vibration, sound, or the like. The sensor amplifier 25a to which the sensor 25 is connected is communicably connected to the diagnostic device 100. The sensor 25 and the sensor amplifier 25a may be provided in the processing machine 200 in advance, or may be attached to the processing machine 200 which is a completed machine later. Further, the sensor amplifier 25a is not limited to being installed in the processing machine 200, and may be installed on the diagnostic device 100 side. The detection unit 225 in FIG. 1 is realized by, for example, a sensor 25 and a sensor amplifier 25a.

The hardware configuration shown in FIG. 2 is an example, and the processing machine 200 does not have to be equipped with all the constituent devices, and may be provided with other constituent devices. For example, the numerical control unit 201 and the communication control unit 221 shown in FIG. 1 may be realized by causing the CPU 20 shown in FIG. 2 to execute a program, that is, by software, or by hardware such as an IC (Integrated Circuit). It may be realized by using software and hardware together.

(Hardware configuration of diagnostic device)
Next, a hardware configuration example of the diagnostic apparatus 100 of the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing an example of the hardware configuration of the diagnostic apparatus 100 according to the embodiment.

As shown in FIG. 3, the diagnostic device 100 includes a CPU 10, a ROM 10a, a RAM 10b, a communication I / F11, a sensor I / F12, an auxiliary storage device 13, an input device 14, and a display 15. It is configured so that it can communicate with 1B.

The CPU 10 is an arithmetic unit that controls the entire diagnostic device 100. The CPU 10 controls the operation of the entire diagnostic apparatus 100 and realizes a diagnostic function by, for example, executing a program stored in the ROM 10a or the like with the RAM 10b as a work area (work area). The processing information acquisition unit 101, the feature extraction unit 102, the generation unit 103, the first identification unit 104, the second identification unit 105, the abnormality determination unit 106, and the display control unit 107 of FIG. 1 operate, for example, in the CPU 10. Realized by the program.

The communication I / F 11 is an interface used for communication with an external device such as a processing machine 200. The communication I / F 11 is, for example, a NIC or the like corresponding to TCP / IP. The communication control unit 111 of FIG. 1 is realized by, for example, the communication I / F 11 shown in FIG. 3 and a program operated by the CPU 10.

The sensor I / F 12 is an interface for receiving detection information from the sensor 25 installed in the processing machine 200 via the sensor amplifier 25a. The detection information receiving unit 112 of FIG. 1 is realized by, for example, a program operated by the sensor I / F12 and the CPU 10.

The auxiliary storage device 13 is an HDD (Hard Disk Drive) or SSD that stores various data such as setting information of the diagnostic device 100, detection information and context information received from the processing machine 200, an OS (Operating System), and an application program. (Solid State Drive), or a non-volatile storage device such as an EEPROM (Electrically Erasable Program Read-Only Memory). The auxiliary storage device 13 is provided in the diagnostic device 100, but is not limited to this, and may be, for example, a storage device installed outside the diagnostic device 100, or the diagnostic device. It may be a storage device provided with a server device capable of data communication with 100. The storage unit 113 of FIG. 1 is realized by, for example, a RAM 10b, an auxiliary storage device 13, or the like.

The input device 14 is an input device such as a mouse or a keyboard that performs operations such as inputting characters and numbers, selecting various instructions, and moving a cursor. The input unit 114 of FIG. 1 is realized by, for example, an input device 14.

The display 15 is a display device such as a CRT (Cathode Ray Tube) display, an LCD (Liquid Crystal Display), or an organic EL (Electro-Luminence) display that displays characters, numbers, various screens, operation icons, and the like. The display unit 115 of FIG. 1 is realized by, for example, the display 15.

The hardware configuration shown in FIG. 3 is an example, and the diagnostic apparatus 100 does not have to be equipped with all the constituent devices, and may be provided with other constituent devices. For example, each functional unit of the diagnostic apparatus 100 shown in FIG. 1 (machining information acquisition unit 101, feature extraction unit 102, generation unit 103, first identification unit 104, second identification unit 105, abnormality determination unit 106, and The display control unit 107) causes the CPU 10 to execute a program, that is, it may be realized by software, it may be realized by hardware such as an IC, or it may be realized by using software and hardware together. May be good. Further, when the diagnostic device 100 specializes in the diagnostic operation of the processing machine 200 and transmits the diagnostic result to an external server device or the like, the input device 14 and the display 15 may not be provided.

(Operation example of the first identification unit)
Next, an operation example of the first identification unit 104 will be described with reference to FIGS. 4 to 9. The first identification unit 104 identifies the processed section from the non-processed section. When the first identification unit 104 identifies the processed section from the non-processed section, the feature extraction unit 102 extracts the feature information from the detection information of the non-processed section.

FIG. 4 is a diagram showing an example of the detection information and the ladder signal of the processing machine 200 according to the embodiment. As shown in FIG. 4, the sensor data (detection information) includes a waveform portion indicating a non-processed section and a waveform portion indicating a processed section. The non-machined section is the section before and after the tool comes into contact with the machined object. The machining section is a section in which the tool comes into contact with the machining target to perform the actual machining process.

On the other hand, the processing machine 200 turns on the ladder signal at the start of the machining operation by the tool, feeds the tool to the machining target, and turns off the ladder signal when the actual machining process is completed. In this case, the processing machine 200 does not turn on the ladder signal after the tool comes into contact with the processing target. For this reason, in the tool feed section where the ladder signal is ON, there is a non-machining section where the tool is not in contact with the machining target and a machining where the tool is in contact with the machining target to perform machining processing. The section will be included.

Since the distance between the machining target and the tool is unknown depending on the machining conditions and the like, it may be difficult to accurately determine the machining section based on such a ladder signal. However, if the detection information of the non-machining section is included in the abnormality determination of the processing machine 200, the accuracy of the determination is lowered. Therefore, it is necessary to accurately estimate the section where machining is actually performed.

For example, when the detection information is frequency data collected by a vibration sensor or a microphone device, the feature extraction unit 102 extracts energy, frequency spectrum, MFCC (mel frequency cepstrum coefficient), and the like as feature information. In the present embodiment, the extracted feature information will be described as being a frequency spectrum.

FIG. 5 is a diagram illustrating feature information extracted from detection information by the diagnostic apparatus 100 according to the embodiment as frequency components. The section shown in FIG. 5 includes one tool feed section. That is, the section of FIG. 5 includes one processing section. In order to estimate one machining section, the feature extraction unit 102 extracts feature information from the detection information of the section including one tool feed section among the detection information received by the detection information receiving unit 112.

Specifically, the feature extraction unit 102 extracts the feature information by, for example, performing a Fourier transform on the detection information for each frame. Here, the frame indicates the amount of data of the detection information for a predetermined time (for example, 20 [ms], 40 [ms], etc.), and is obtained by, for example, Fourier transforming the feature information with respect to the detection information. It corresponds to the amount of data of the window length in the case of the frequency spectrum to be obtained. The feature information shown in FIG. 5 is associated with the frame time of the corresponding detection information. The frequency spectrum of the processed section has characteristics different from the frequency spectrum of the unprocessed section. However, in FIG. 5, since the difference between the characteristic information of the non-processed section and the characteristic information of the processed section is schematically shown, the frequency component is not shown in the non-processed section. This does not mean that there is no frequency component in the unprocessed section.

The selection unit 104a of the first identification unit 104 selects the feature information of the non-processed section from the feature information extracted as described above in order to generate a model of the non-processed section. Further, the selection unit 104a selects the target feature information from the feature information extracted as described above in order to compare it with the model of the non-processed section.

FIG. 6 is a diagram illustrating an example in which the diagnostic apparatus 100 according to the embodiment selects the feature information of the non-processed section and the processed section. In FIG. 6, the feature information corresponding to the frame of the selected portion 401 is selected in the feature information of the non-processed section, and the feature information corresponding to the frame of the selected portion 402 is selected in the feature information of the processed section.

When selecting the feature information of the non-processed section, the selection unit 104a selects, for example, the feature information of the section in which the ladder signal is OFF, as in the selection portion 401. However, the selection of the feature information of the non-processed section by the selection unit 104a is not limited to this. For example, since there is a high possibility that the section immediately after the ladder signal is turned on is also a non-processed section, the selection unit 104a may select the feature information of the section. Alternatively, the selection unit 104a may select information generated by machine learning for the feature information included in the non-processed section as the feature information of the non-processed section. A model is generated by the generation unit 103 from the feature information of the non-processed section selected in this way.

On the other hand, when selecting the feature information of the machining section, the selection section 104a may select, for example, the feature information of the latter half of the tool feed section in which the ladder signal is ON, as in the selection section 402. This is because the first half of the tool feed section may include a non-machined section. The selection unit 104a sequentially selects the feature information in frame units in order to compare with the model generated from the feature information of the non-machined section in the first half portion of the tool feed section. When the selection unit 104a sequentially selects the feature information in frame units, it does not prevent the frames from overlapping.

FIG. 7 is a diagram illustrating the feature information selected from the non-processed section and the processed section by the diagnostic apparatus 100 according to the embodiment in a frequency spectrum. FIG. 7 shows a frequency spectrum as feature information corresponding to the frame of the selection portion 401 and a frequency spectrum as feature information corresponding to the frame of the selection portion 402. Since the frequency spectrum of the selected portion 402 is the frequency spectrum in the machining section where the tool is in contact with the machining target and the machining process is performed, the frequency spectrum has a large amplitude in a specific frequency component. On the other hand, since the frequency spectrum of the selected portion 401 is a frequency spectrum in the non-machining section where the tool is not in contact with the machining target, the frequency spectrum has a small amplitude as a whole.

The calculation unit 104b of the first identification unit 104 compares the model corresponding to the non-processed section with the target feature information of the section that may be the processed section, and calculates the uniqueness of the processed section of each target feature information. do. As a method of comparing the model with the target feature information, in addition to the method of obtaining and comparing the Euclidean distance, machine learning such as a mutual correlation coefficient or GMM (Gaussian Mixture Model) is used.

FIG. 8 is a diagram showing an example in which the diagnostic apparatus 100 according to the embodiment calculates the Euclidean distance to be a machined section. In FIG. 8, it is shown that the closer the machining section is to “1”, the higher the likelihood of the machining section. Since the processing section-likeness is calculated from the feature information extracted from the detection information including noise, it is a value including variation as shown in FIG.

The section determination unit 104c of the first identification unit 104 determines whether or not the processing section-likeness of the individual feature information calculated by the calculation unit 104b is larger than a predetermined threshold value.

FIG. 9 is a diagram illustrating an operation of estimating a machining section from the machining section likeness calculated by the diagnostic apparatus 100 according to the embodiment. As shown in FIG. 9, the section determination unit 104c estimates the “estimated machining section” in which the machining section-likeness is larger than a predetermined threshold value as the machining section. On the contrary, the section determination unit 104c estimates a section having a processed section likeness smaller than the threshold value as a non-processed section. In addition, in order to estimate the processing section, the section determination unit 104c performs threshold value determination for the uniqueness of the processing section, but is not limited to this, and for example, processing is performed using a statistical method. Judgment may be made for the section-likeness.

The first identification unit 104 estimates a section that is determined by the section determination unit 104c to be larger than a predetermined threshold value as a processing section.

(Operation example of the second identification unit)
Next, an operation example of the second identification unit 105 will be described with reference to FIGS. 7 to 9. The second identification unit 105 discriminates between the non-processed section and the processed section by using the identification result by the first identification unit 104.

The feature information collecting unit 105d of the second identification unit 105 collects the feature information of the non-processed section and the processed section specified from the identification result by the first identification unit 104. After a certain period of time has passed after the processing machine 200 is started up or after the tool is changed, a predetermined number of feature information shown in FIG. 7 is collected. The feature information collecting unit 105d sets a flag when a sufficient number of data for generating a model from each feature information is obtained. Using this flag as a trigger, the generation unit 103 generates a model of the non-machining section and a model of the machining section, respectively, from the collected predetermined number of feature information.

The selection unit 105a of the second identification unit 105 selects the feature information extracted based on the detection information of the non-processed section. Further, the selection unit 105a selects the feature information extracted based on the detection information of the section that may be the machining section. That is, the selection unit 105a selects the feature information corresponding to the frame of the selection portion 401 in FIG. 7 and the feature information corresponding to the frame of the selection portion 402.

The calculation unit 105b of the second identification unit 105 compares the model of the non-processed section with the plurality of feature information selected by the selection unit 105a, and calculates the uniqueness of the processed section of each feature information. Further, the calculation unit 105b compares the model of the processing section with the above-mentioned feature information, and calculates the uniqueness of the processing section of each feature information. That is, as shown in FIG. 8, the individual feature information is compared with both the model of the non-machined section and the model of the machined section, and the uniqueness of the machined section is calculated.

As shown in FIG. 9, the section determination unit 105c of the second identification unit 105 determines whether or not the processing section-likeness of the individual feature information calculated by the calculation unit 105b is larger than a predetermined threshold value.

The second identification unit 105 estimates a section determined by the section determination unit 105c to be larger than a predetermined threshold value as a processing section.

(Example of abnormality judgment processing at the initial stage of machining)
Next, an example of the abnormality determination process of the processing machine 200 at the initial stage of processing will be described with reference to FIGS. 10 and 11. FIG. 10 is a flow chart showing an example of a procedure of abnormality determination processing at the initial stage of processing by the diagnostic apparatus 100 according to the embodiment.

Prior to the abnormality determination, first, a model when the processing machine 200 is normal is generated.

As shown in FIG. 10, in step S110, the machining information acquisition unit 101 acquires context information from the processing machine 200 via the communication control unit 111. In step S120, the detection information receiving unit 112 acquires the detection information from the detection unit 225. The context information and the detection information acquired at this time are information when the processing machine 200 is operating normally.

In step S130, the feature extraction unit 102 extracts the feature information of the section including the non-processed section and the processed section from the predetermined section by the ladder signal.

In step S140, the first identification unit 104 estimates the machining section.

In step S150, the generation unit 103 generates a model from the estimated feature amount of the processing section.

In step S160, the first identification unit 104 makes a determination to switch to the second identification unit 105. Specifically, the first identification unit 104 determines whether or not a flag indicating that sufficient data has been collected by the feature information collection unit 105d of the second identification unit 105 is set. At this stage, since sufficient data collection has not been performed (No), the first identification unit 104 does not switch to the second identification unit 105, and returns to step S101.

Next, the diagnostic apparatus 100 determines the abnormality of the processing machine 200.

In steps S110 and S120, context information and detection information are acquired. These context information and detection information are acquired when the abnormality determination of the processing machine 200 becomes necessary.

In step S130, the feature extraction unit 102 extracts the feature information of the section including the non-processed section and the processed section.

In step S140, the first identification unit 104 estimates the machining section.

In step S150, the abnormality determination unit determines the abnormality of the processing machine 200 by comparing the previously generated model of the machining section in the normal state with the feature information based on the detection information of the machining section acquired this time. ..

In step S160, the first identification unit 104 makes a determination to switch to the second identification unit 105. If a flag indicating that sufficient data has been collected by the feature information collecting unit 105d of the second identification unit 105 is set (Yes), the first identification unit 104 switches to the second identification unit 105. conduct.

As a result, the abnormality determination process of the processing machine 200 at the initial stage of processing is completed. After that, the diagnostic apparatus 100 discriminates between the non-processed section and the processed section by the function of the second identification unit 105, and makes an abnormality determination.

Next, the details of step S140 will be described with reference to FIG.

FIG. 11 is a flow chart showing an example of a procedure for estimating a processing section by the first identification unit 104 according to the embodiment. As shown in FIG. 11, in step S141, the selection unit 104a selects the feature information of the non-processed section. In step S142, the generation unit 103 generates a model of the non-processed section from the feature information of the non-processed section.

In step S143, the selection unit 104a sequentially selects the target feature information from the section that may be the machining section. The calculation unit 104b compares these target feature information with the model of the non-processed section, and obtains outliers. In step S144, the calculation unit 104b further calculates the uniqueness of the processing section for each target feature information.

In step S145, the section determination unit 104c estimates the section determined to be more like a machining section than a predetermined threshold value as the machining section.

As a result, the estimation process of the processing section by the first identification unit 104 is completed.

(Example of abnormality judgment processing after the lapse of a predetermined period)
After a predetermined period has elapsed from the initial stage of processing, the second identification unit 105 identifies the non-processed section and the processed section, and the diagnostic apparatus 100 generates a model and determines an abnormality.

FIG. 12 is a flow chart showing an example of a procedure for abnormality determination processing after a lapse of a predetermined period by the diagnostic apparatus 100 according to the embodiment. In step S200, models of the non-machined section and the machined section are generated, respectively. Such a model is generated from a predetermined number of feature information collected by the feature information collecting unit 105d.

In steps S210 and S220, the context information and the detection information of the processing machine 200 that requires abnormality determination are acquired.

In step S230, the feature extraction unit 102 extracts the feature information of the section including the non-processed section and the processed section.

In step S240, the second identification unit 105 estimates the machining section.

In step S250, the abnormality determination unit determines the abnormality of the processing machine 200 by comparing the model of the processing section generated in step S200 with the feature information based on the detection information acquired in step S220.

As a result, the abnormality determination process of the processing machine 200 after the elapse of the predetermined period is completed.

Next, the details of step S240 will be described with reference to FIG.

FIG. 13 is a flow chart showing an example of a procedure for estimating the processing section by the second identification unit 105 according to the embodiment. As shown in FIG. 13, in step S421, the calculation unit 105b compares the model of the non-processed section generated in step S200 of FIG. 12 with each target feature amount. Further, the model of the processing section in step S200 is compared with each target feature amount, and an approximate value for each feature information is obtained for the model. In step S242, the calculation unit 105b further calculates the uniqueness of the processing section for each target feature information.

In step S243, the section determination unit 105c estimates a section determined to be more like a machining section than a predetermined threshold value as a machining section.

As a result, the estimation process of the processing section by the second identification unit 105 is completed.

For example, in the information acquisition device of Patent Document 1, time-series data of machine information such as a spindle load and event data of the device such as a program name, a tool number, and an override value by an operator operation are output in correspondence with each other. As a result, an abnormality in the machining operation is detected. However, it has been difficult to accurately grasp the actual machining section by separating the non-machining section and the machining section from the event data of the apparatus.

In the diagnostic apparatus 100 of the embodiment, even in the initial stage of machining, a model of the non-machined section is generated from a small amount of feature information, and the machined section is identified from the non-machined section. As a result, a predetermined accuracy can be obtained in the interval estimation.

Further, in the diagnostic apparatus 100 of the embodiment, after a predetermined period has elapsed from the initial stage of processing, a model is generated for both the non-processed section and the processed section based on the abundant feature information obtained up to that point, and both sections are generated. Used for identification. This makes it possible to grasp the actual machining section with higher accuracy.

As described above, in the diagnostic apparatus 100 of the embodiment, since appropriate algorithms are used at the initial stage of processing and after the lapse of a predetermined period, more efficient estimation of the processing section can be performed.

Further, in the processing machine 200, as shown in FIG. 14, processing using the same tool may be continuously performed a plurality of times. In FIG. 14, for example, steps 1-1 and 1-2 are the same, steps 1-4 to 1-6 are the same, and steps 1-7 to 1-9 are the same. Such a process is processing based on the same context information. Even in such a case, the first identification unit 104 and the second identification unit 105 of the embodiment can discriminate between the non-processed section and the processed section, and can estimate the processed section with high accuracy.

(Modification example)
In the above-described embodiment, when the first identification unit 104 generates a model of the non-processed section in order to distinguish between the non-processed section and the processed section, the feature information of the actual non-processed section is used. Not limited to this. Before the start of machining, the machining machine 200 may be operated in advance in the absence of a workpiece to acquire feature information. Using this as feature information in the non-processed section, a model of the non-processed section can be generated.

In the above-described embodiment, the detection information is, for example, vibration data, acoustic data, or the like, but other data such as the current value, load, and torque of the motor can also be used as the detection information.

In the above-described embodiment, the device to be diagnosed is, for example, the processing machine 200, but a machine such as an assembly machine, a measuring machine, an inspection machine, or a washing machine may be the target device.

The program executed by the above-described embodiment and the diagnostic system of each modification may be configured to be provided by being incorporated in a ROM or the like in advance.

Further, the program executed by the diagnostic system of the above-described embodiment and each modification is a CD-ROM (Computer Disc Read Only Memory), a flexible disk (FD), or a CD in an installable format or an executable format file. -R (Compact Disk-Recordable), DVD (Digital Versaille Disk) or the like may be configured to be recorded on a computer-readable recording medium and provided as a computer program product.

Further, the program executed by the diagnostic system of the above-described embodiment and each modification may be stored on a computer connected to a network such as the Internet and provided by downloading via the network. .. Further, the program executed by the above-described embodiment and the diagnostic system of each modification may be provided or distributed via a network such as the Internet.

Further, the program executed in the above-described embodiment and the diagnostic system of each modification has a module configuration including each of the above-mentioned functional units, and as actual hardware, the CPU (processor) reads the program from the ROM. Each of the above-mentioned functional units is loaded on the main storage device, and each functional unit is generated on the main storage device.

1 Diagnostic system 1B bus 2B bus 10 CPU
10a ROM
10b RAM
11 Communication I / F
12 Sensor I / F
13 Auxiliary storage device 14 Input device 15 Display 20 CPU
20a ROM
20b RAM
21 Communication I / F
23 Drive control circuit 24 Motor 24a Tool 25 Sensor 25a Sensor amplifier 100 Diagnostic device 101 Machining information acquisition unit 102 Feature extraction unit 103 Generation unit 104 First identification unit 105 Second identification unit 106 Abnormality determination unit 107 Display control unit 111 Communication Control unit 112 Detection information receiving unit 113 Storage unit 114 Input unit 115 Display unit 200 Processing machine 201 Numerical control unit 221 Communication control unit 223 Drive control unit 224 Drive unit 225 Detection unit

JP-A-2017-0333346

Claims (7)

The first acquisition unit that acquires the detection information output from the detection unit that detects the physical quantity that changes according to the operation of the target device, and
A second acquisition unit that acquires a signal indicating a section for operating the target device from the target device, and
A first identification unit that discriminates between a non-processed section and a processed section in the target device based on the detection information and the signal.
A second identification unit for identifying a non-processed section and a processed section in the target device based on the detection information and the identification result by the first identification unit is provided.
Processing section determination device. An extraction unit that extracts feature information from the detection information of the non-processed section selected by the first identification unit based on the signal, and an extraction unit.
The feature information of the non-processed section is used for learning to generate a first model, and the first model is provided with a generation unit for discriminating between the non-processed section and the processed section.
The processing section determination device according to claim 1. The first acquisition unit is
The detection information corresponding to the detection information of the non-processed section, which is obtained by operating the target device in advance in the absence of the object to be processed, is acquired.
An extraction unit that extracts feature information from the detection information in the pre-operation,
The feature information is used for learning to generate a first model in advance, and the first model is provided with a generation unit for distinguishing between the non-processed section and the processed section.
The processing section determination device according to claim 1. An extraction unit that extracts feature information from the detection information of the non-processed section and the processed section identified by the first identification unit, and an extraction unit.
The feature information of the non-processed section is used for learning to generate a second model, the feature information of the processed section is used for learning to generate a third model, and the second and third models are used for the non-processed section. Used to identify the processing section and the processing section.
The processing section determination device according to any one of claims 1 to 3. It is a processing section determination method executed by the processing section determination device that determines the processing section of the target device.
The first acquisition step of acquiring the detection information output from the detection unit that detects the physical quantity that changes according to the operation of the target device, and the first acquisition unit.
A second acquisition step in which the second acquisition unit acquires a signal indicating a section for operating the target device from the target device, and
The first identification unit identifies the non-processed section and the processed section in the target device based on the detection information and the signal, and the first identification step.
The second identification unit has a second identification step for identifying a non-processed section and a processed section in the target device based on the detection information and the identification result by the first identification step.
Processing section determination method. On the computer
The first acquisition step of acquiring the detection information output from the detection unit that detects the physical quantity that changes according to the operation of the target device, and
A second acquisition step of acquiring a signal indicating a section for operating the target device from the target device, and
A first identification step for identifying a non-processed section and a processed section in the target device based on the detection information and the signal,
Based on the detection information and the identification result by the first identification step, the second identification step for identifying the non-processed section and the processed section in the target device is executed.
program. The first acquisition unit that acquires the detection information output from the detection unit that detects the physical quantity that changes according to the operation of the target device, and
A second acquisition unit that acquires a signal indicating a section for operating the target device from the target device, and
A first identification unit that discriminates between a non-processed section and a processed section in the target device based on the detection information and the signal.
Based on the detection information and the identification result by the first identification unit, the second identification unit that identifies the non-processed section and the processed section in the target device, and the second identification unit.
An abnormality determination unit for determining the presence or absence of an abnormality in the target device by using the identified detection information of the processing section is provided.
Diagnostic device.

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