JP7729259B2 - Diagnostic device, system, and method for controlling the diagnostic device - Google Patents

Description

This disclosure relates to a diagnostic device that performs abnormality diagnosis on a diagnostic target included in an item transport facility.

The material transport equipment that makes up product production lines is required to operate at high capacity without unscheduled shutdowns to prevent production from stalling. For this reason, technologies for detecting abnormalities in the equipment and parts included in material transport equipment have been known.

Patent Document 1 discloses a self-diagnosis method for a traveling carriage system in which a traveling carriage for transporting goods travels along a travel path.

JP 2011-221687 A

The technology disclosed in Patent Document 1 requires that all bogies be equipped with abnormality detection sensors and that diagnostics be performed using the acquired data, resulting in large-scale and complex processing.

One aspect of the present invention was conceived in light of the above-mentioned problems, and its purpose is to realize a diagnostic device or the like that accurately diagnoses abnormalities in a diagnostic object with a simple configuration.

A diagnostic device according to one aspect of the present disclosure includes an acquisition unit that acquires one or more types of measurement data used to diagnose an abnormality in a diagnostic object included in an item conveying facility; a calculation unit that calculates a standard deviation change rate, which is the rate of change between a first standard deviation of multiple measurement data of the same type measured during a predetermined conveying operation in a first predetermined period and a second standard deviation of multiple measurement data of the same type as the first standard deviation measured during the predetermined conveying operation in a second predetermined period after the first predetermined period; and a diagnostic unit that performs an abnormality diagnosis of the diagnostic object based on the calculated standard deviation change rate.

Furthermore, a diagnostic device according to one aspect of the present disclosure includes an acquisition unit that acquires measurement data indicating at least one of a current value, vibration amount, sound volume, and stop position of a transport vehicle to be used for diagnosing an abnormality in a diagnostic object included in an item transport facility; a calculation unit that calculates a degree of change in average value, which is the degree of change between a first average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period and a second average value of a plurality of measurement data of the same type as the first measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period; and a diagnostic unit that performs an abnormality diagnosis of the diagnostic object based on the calculated degree of change in average value.

Furthermore, a control method for a diagnostic device according to one aspect of the present disclosure includes an acquisition step of acquiring one or more types of measurement data used for diagnosing an abnormality in a diagnostic object included in an item conveying facility; a calculation step of calculating a standard deviation change rate, which is the rate of change between a first standard deviation of multiple measurement data of the same type measured during a predetermined conveying operation in a first predetermined period and a second standard deviation of multiple measurement data of the same type as the acquired measurement data measured during the predetermined conveying operation in a second predetermined period after the first predetermined period; and a diagnosis step of diagnosing an abnormality in the diagnostic object based on the calculated standard deviation change rate.

Furthermore, a control method for a diagnostic device according to one aspect of the present disclosure is a method including: an acquisition step of acquiring measurement data indicating at least one of a current value, vibration amount, sound volume, and stop position of a transport vehicle to be used for diagnosing an abnormality in a diagnostic object included in an item transport facility; a calculation step of calculating a degree of change in average value, which is the degree of change between a first average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period and a second average value of a plurality of measurement data of the same type as the first average value measured during the predetermined transport operation in a second predetermined period after the first predetermined period; and a diagnosis step of diagnosing an abnormality in the diagnostic object based on the calculated degree of change in average value.

Abnormalities in the subject can be diagnosed accurately with a simple configuration.

1 is a diagram showing an example of a friction-driven article transport facility that is the premise of an embodiment of the present invention; 1 is a diagram showing an example of a chain-driven article transport facility that is the premise of an embodiment of the present invention; 1 is a functional block diagram showing a configuration of a main part of a diagnostic device according to an embodiment of the present invention; FIG. 10 is a diagram illustrating an example of a change in standard deviation of measurement data. FIG. 10 is a functional block diagram showing the configuration of a main part of a diagnostic device according to another embodiment. FIG. 10 is a diagram illustrating an example of a change in the average value of measurement data. FIG. 10 is a functional block diagram showing the configuration of a main part of a diagnostic device according to yet another embodiment. FIG. 10 is a diagram illustrating an example of changes in the standard deviation and average value of measurement data. FIG. 1 is a diagram illustrating an overview of an operation control device. FIG. 2 is a diagram for explaining the details of operation control by the operation control device. FIG. 1 is a simplified diagram showing a friction-driven article transport facility. FIG. 12 is a diagram showing details of area A in FIG. 11 . 12 is a diagram showing the relationship between the wheels attached to the bogie and the rails shown in area B of FIG. 11 . FIG. FIG. 1 is a simplified diagram showing a chain-driven article transport facility. 10A and 10B are diagrams showing the configuration of carriages in another example of an article transport facility. FIG. 10 is a diagram showing an example of a change in the average value of stop position data when the measurement data is stop position data. FIG. 10 is a diagram showing an example of a change in the standard deviation of stop position data when the measurement data is stop position data. 10A and 10B are diagrams illustrating an example of changes in the standard deviation and average value of stop position data when the measurement data is stop position data. FIG. 10 is a diagram showing an example of a change in the average value of current data when the measurement data is current data. 10A and 10B are diagrams illustrating an example of changes in the standard deviation and average value of current data when the measurement data is current data. 10A and 10B are diagrams illustrating an example of changes in the standard deviation and average value of current data when the measurement data is current data. 10A and 10B are diagrams illustrating an example of changes in the average value of vibration data or sound data when the measurement data is vibration data or sound data. 10 is a flowchart showing an example of a processing flow of a diagnostic device. 10 is a flowchart showing an example of a processing flow of a diagnostic device.

[Embodiment 1]
〔overview〕
An overview of the diagnostic device 10 according to this embodiment will be described. The diagnostic device 10 performs abnormality diagnosis on an article transport facility 1 including a conveyor or the like for transporting articles, such as vehicle bodies, which is installed in an automobile manufacturing plant or the like. The equipment to be diagnosed by the diagnostic device 10 may be any equipment that transports articles using a conveyor or the like.

First, with reference to Figures 1 and 2, an example of an item conveying facility 1 to be diagnosed by the diagnostic device 10 according to this embodiment will be described. Figure 1 shows, as the item conveying facility 1, a facility that conveys items using a friction-driven conveyor. Figure 2 shows a facility that conveys items using a chain-driven conveyor.

[Friction drive type]
As described above, Fig. 1 shows an example of an article conveying facility 1, which is a friction-driven conveying facility 511. The conveying facility 511 has a plurality of travel devices 531 arranged along lower guide rails 514, which act on the load bar (not shown) of each carriage (transport vehicle) 515 (corresponding to carriage 23 in Fig. 11 ) to cause the carriage 515 to travel. Note that one of the travel devices 531 is always arranged to act on the load bar of the carriage 515 guided by the lower guide rails 514. Each traveling device 531 is composed of friction drive wheels 532 (corresponding to drive rollers 24 in FIG. 12) and backup rollers 533 (corresponding to backup rollers 21 in FIG. 12) that sandwich the driven side of the load bar from both the left and right, and a motor (not shown) that drives the friction drive wheels 532. The friction drive wheels 532 and backup rollers 533 are supported so that they can move laterally in a horizontal direction approximately perpendicular to the path of movement of the load bar, i.e., the conveying line p, and are biased toward the load bar by springs (not shown), so that the friction drive wheels 532 are securely in pressure contact with the driven side of the load bar. In addition, each traveling device 531 is provided with an occupancy detector 536 consisting of a magnetic sensor that detects the presence (or absence) of the cart 515 by the presence or absence of the load bar.

[Chain drive type]
As described above, FIG. 2 illustrates an example of an article conveying facility 1, specifically, a chain-driven conveying facility 610. In the conveying facility 610, a chain-driven section 630 equipped with a drive chain 624 (corresponding to the chain 30 in FIG. 14 ) and a friction-driven section 631 are set along the circular travel path of a conveying vehicle (not shown). The drive system in the friction-driven section 631 is the same as that in the conveying facility 511 in FIG. 1 described above. In the friction-driven section 631, friction-driven devices 632 are arranged along the travel path of the conveying vehicle at intervals not longer than the overall length of the load bar (not shown). The friction-driven devices 632 are composed of a friction-driven wheel, a motor with a reducer that rotates the friction-driven wheel, and a biasing device that presses the friction-driven wheel against one side of the friction-driven surface of the load bar of the conveying vehicle. The drive chain 624 is tensioned so as to rotate along the chain drive section 630. In a return path 645 that runs from the end to the start of the chain drive section 630, the drive chain 624 is driven by a drive means 646 (corresponding to the drive device 28 in FIG. 14) and tensioned by a take-up means 647 to maintain an appropriate tension. Also, fixed stop positions 648a, 648b are set within the chain drive section 630.

〔others〕
Note that the item transport equipment 1 including the diagnosis target of the diagnosis device 10 according to this embodiment is not limited to the two examples described above. The item transport equipment 1 may also be a transfer machine called a stacker crane. In the case of a stacker crane, a cart that runs on a running rail on the floor and a lifting platform that can be raised and lowered are provided, and items are transferred between the lifting platform and multiple storage sections arranged vertically and horizontally within the item storage shelf by the traveling operation of the cart and the lifting operation of the lifting platform. Furthermore, the stacker crane may be provided with guide rails on the ceiling side to guide the travel of the cart. Furthermore, a lifting guide may be provided to guide the lifting operation of the lifting platform.

[Configuration of main parts of diagnostic device 10]
Next, the diagnostic device 10 will be described with reference to Fig. 3. Fig. 3 is a functional block diagram showing the configuration of the main parts of the diagnostic device 10. The diagnostic device 10 is a device that performs abnormality diagnosis on one or more diagnostic objects 2 included in the item conveying equipment 1. As shown in Fig. 3, the diagnostic device 10 includes an acquisition unit 11, a calculation unit 12, and a diagnosis unit 13.

The acquisition unit 11 acquires one or more types of measurement data used for diagnosing abnormalities in the diagnosis object 2 included in the item transport equipment 1. Examples of the measurement data include the following. In this specification, the term "vicinity" includes the meanings of "adjacent position" and "close position."
Vibration data: Data indicating the amount of vibration in the diagnostic object 2 or in the vicinity of the diagnostic object 2.
Current data: data indicating the value of the current supplied to the diagnostic object 2.
Sound data: data indicating the volume of sound collected near the diagnostic object 2.
Stop position data: Data indicating the stopping position of the carriage (transport vehicle).

Vibration data can be obtained by installing a vibrometer (e.g., an acceleration sensor) on or near the diagnosis object 2. Current data can be obtained by measuring the current supplied to the diagnosis object 2 with an ammeter. A typical example of a diagnosis object 2 to which current is supplied is the motor 25. Sound data can be obtained by installing a sound collection device (microphone) on or near the diagnosis object 2. Stop position data can be obtained by attaching an encoder to the running wheels of the bogie or the motor that drives the bogie. Hereinafter, the vibrometer, ammeter, sound collection device, and encoder will be collectively referred to as sensors 3.

The acquisition unit 11 acquires each measurement data obtained by a sensor 3 installed on or near the diagnostic object 2.

The calculation unit 12 calculates data from the measurement data acquired by the acquisition unit 11 that is used by the diagnosis unit 13 (described later) to diagnose abnormalities in the diagnosis object 2, and includes a standard deviation change degree calculation unit 121.

The standard deviation change degree calculation unit 121 calculates the standard deviation from the measurement data acquired by the acquisition unit 11, and calculates the degree of change in the calculated standard deviation. Specifically, the following processes (1) to (3) are performed.
(1) A first standard deviation is calculated, which is the standard deviation of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period.
(2) A second standard deviation is calculated, which is the standard deviation of multiple measurement data of the same type as the measurement data used to calculate the first standard deviation, measured during a specified transport operation in a second specified period after the first specified period.
(3) Calculate the degree of change in standard deviation, which is the degree of change between the first standard deviation and the second standard deviation.

The measurement data for which the standard deviation is calculated may be the maximum value for one transport operation. For example, if there are 10 transport operations in a specified period and the maximum values of the measurement data for each transport operation are M1, M2, M3, ..., M10, the standard deviation change degree calculation unit 121 calculates the standard deviation of "M1, M2, M3, ..., M10" as the standard deviation of the measurement data.

The standard deviation change degree calculation unit 121 may calculate the ratio of the second standard deviation to the first standard deviation as the degree of change in standard deviation. That is, if the first standard deviation is Sa and the second standard deviation is Sb, the standard deviation change degree calculation unit 121 may calculate the degree of change in standard deviation as Sb/Sa.

The diagnosis unit 13 diagnoses an abnormality of the diagnosis object 2 according to the degree of change in standard deviation calculated by the calculation unit 12. For example, the diagnosis unit 13 may diagnose that the diagnosis object is abnormal or may be abnormal when the degree of change in standard deviation calculated by the standard deviation change degree calculation unit 121 satisfies a predetermined condition. Whether the predetermined condition is satisfied may be determined by whether the degree of change in standard deviation exceeds a threshold. Note that the diagnosis unit 13 may diagnose one or more diagnosis objects 2.

Next, with reference to Figure 4, an example of the calculation of the standard deviation change rate by the standard deviation change rate calculation unit 121 and an example of the diagnosis by the diagnosis unit 13 will be described. Figure 4 is a diagram showing an example of the change in standard deviation, with the horizontal axis representing the measurement data value and the vertical axis representing the number of measurement data values over a predetermined period (first predetermined period or second predetermined period). The number of measurement data values is the number of measurement data values measured over a predetermined period. Graph 401 in Figure 4 shows an example of measurement data over the first predetermined period. Graph 402 in Figure 4 shows an example of measurement data over the second predetermined period. As shown in graph 401, the standard deviation of the measurement data over the first predetermined period is S1. As shown in graph 402, the standard deviation of the measurement data over the second predetermined period is S2 (>S1). Note that AV1 is the average value of the measurement data.

The degree of change in standard deviation calculated by the standard deviation change degree calculation unit 121 is the ratio of the second standard deviation S2 to the first standard deviation S1, and is therefore S2/S1. Here, if the threshold value used by the diagnosis unit 13 to diagnose an abnormality is Th1, when S2/S1 exceeds the threshold value Th1, the diagnosis unit 13 diagnoses that the object to be diagnosed has an abnormality or there is a possibility of an abnormality.

[Embodiment 2]
The second embodiment will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the first embodiment, and the description thereof will not be repeated.

Figure 5 is a functional block diagram of a diagnostic device 10A according to this embodiment. As shown in Figure 5, the diagnostic device 10A differs from the diagnostic device 10 described above in that it includes a calculation unit 12A instead of the calculation unit 12.

The calculation unit 12A calculates data from the measurement data acquired by the acquisition unit 11 that is used by the diagnosis unit 13 (described later) to diagnose abnormalities in the diagnosis object 2, and includes an average value change degree calculation unit 122.

The average value change degree calculation unit 122 calculates an average value from the measurement data acquired by the acquisition unit 11, and calculates the degree of change in the calculated average value. Specifically, the following processes (1) to (3) are performed.
(1) A first average value is calculated, which is the average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period.
(2) A second average value is calculated, which is the average value of multiple measurement data of the same type as the measurement data used to calculate the first average value, measured during a specified transport operation in a second specified period after the first specified period.
(3) Calculate the degree of change in the average value, which is the degree of change between the first average value and the second average value.

The measurement data for which the average value is calculated may be the maximum value for one transport operation. For example, if there are 10 transport operations in a predetermined period and the maximum values of the measurement data for each transport operation are M1, M2, M3, ..., M10, the average value change degree calculation unit 122 calculates the average value of "M1, M2, M3, ..., M10" as the average value of the measurement data.

The average value change degree calculation unit 122 may calculate the difference between the first average value and the second average value as the average value change degree. That is, if the first average value is AVa and the second average value is AVb, the average value change degree calculation unit 122 may calculate the average value change degree as |AVa - AVb| (the absolute value of the difference).

The diagnosis unit 13 diagnoses an abnormality of the diagnosis object 2 according to the degree of change in the average value calculated by the calculation unit 12A. For example, the diagnosis unit 13 may diagnose that the diagnosis object is abnormal or may be abnormal when the degree of change in the average value calculated by the average value change degree calculation unit 122 satisfies a predetermined condition. Whether the predetermined condition is satisfied may be determined by whether the degree of change in the average value exceeds a threshold. Note that the diagnosis unit 13 may diagnose one or more diagnosis objects 2.

Next, referring to Figure 6, an example of calculation of the average value by the average value change degree calculation unit 122 and diagnosis by the diagnosis unit 13 will be described. Figure 6 is a diagram showing an example of change in the average value, with the horizontal axis representing the measurement data value and the vertical axis representing the number of measurement data values in a predetermined period (first predetermined period or second predetermined period). The number of measurement data values is the number of measurement data values measured in a predetermined period. Graph 601 in Figure 6 shows an example of measurement data in the first predetermined period. Graph 602 in Figure 6 shows an example of measurement data in the second predetermined period. As shown in graph 601, the average value of the measurement data in the first predetermined period is AV2. As shown in graph 602, the average value of the measurement data in the second predetermined period is AV3 (>AV2).

The average value degree calculated by the average value change degree calculation unit 122 is the difference D1 between the first average value AV2 and the second average value AV3, so D1 = |AV2 - AV3|. Here, if the threshold value used by the diagnosis unit 13 to diagnose an abnormality is Th2, when D1 exceeds Th2 (D1 > Th2), the diagnosis unit 13 diagnoses that the object to be diagnosed has an abnormality or there is a possibility of an abnormality.

[Embodiment 3]
The third embodiment will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the first and second embodiments, and the description thereof will not be repeated.

Figure 7 is a functional block diagram of a diagnostic device 10B according to this embodiment. As shown in Figure 7, diagnostic device 10B differs from the diagnostic devices 10 and 10A described above in that it includes a calculation unit 12B instead of calculation units 12 and 12A.

The calculation unit 12B calculates data from the measurement data acquired by the acquisition unit 11 that is used by the diagnosis unit 13 (described later) to diagnose abnormalities in the diagnosis object 2, and includes a standard deviation change degree calculation unit 121 and a mean value change degree calculation unit 122.

As described above, the standard deviation change degree calculation unit 121 calculates the standard deviation from the measurement data acquired by the acquisition unit 11 and calculates the degree of change in the calculated standard deviation.

As described above, the average value change degree calculation unit 122 calculates the average value from the measurement data acquired by the acquisition unit 11 and calculates the degree of change in the calculated average value.

The diagnosis unit 13 diagnoses an abnormality of the diagnosis object 2 according to the degree of change in standard deviation and the degree of change in mean value calculated by the calculation unit 12B. For example, the diagnosis unit 13 may diagnose that the diagnosis object is abnormal or may be abnormal when the combination of the degree of change in standard deviation calculated by the standard deviation change degree calculation unit 121 and the degree of change in mean value calculated by the mean value change degree calculation unit 122 satisfies a predetermined condition. Whether the predetermined condition is satisfied may be determined based on whether the degree of change in standard deviation exceeds a threshold and whether the degree of change in mean value exceeds a threshold. Note that the diagnosis unit 13 may diagnose one or more diagnosis objects 2.

Next, with reference to FIG. 8, an example of the calculation of the standard deviation by the standard deviation change degree calculation unit 121, the calculation of the average value by the average value change degree calculation unit 122, and the diagnosis by the diagnosis unit 13 will be described. FIG. 8 is a diagram showing an example of changes in the standard deviation and average value, with the horizontal axis representing the measurement data value and the vertical axis representing the number of measurement data values in a predetermined period (first predetermined period or second predetermined period). The number of measurement data values is the number of measurement data values measured in a predetermined period. Graph 801 in FIG. 8 shows an example of measurement data in the first predetermined period. Graph 802 in FIG. 8 shows an example of measurement data in the second predetermined period. As shown in graph 801, the standard deviation of the measurement data in the first predetermined period is S3, and the average value is AV4. As shown in graph 802, the standard deviation of the measurement data in the second predetermined period is S4 (>S3), and the average value is AV5 (>AV4).

The standard deviation change degree calculated by the standard deviation change degree calculation unit 121 is the ratio of the second standard deviation S4 to the first standard deviation S3, so it is S4/S3. Furthermore, the average value degree calculated by the average value change degree calculation unit 122 is the difference D2 between the first average value AV4 and the second average value AV5, so it is D2 = |AV4 - AV5|. Here, if the standard deviation threshold and the average value threshold used by the diagnosis unit 13 to diagnose an abnormality are Th3 and Th4, respectively, when S4/S3 exceeds the threshold Th3 and D2 exceeds Th4, the diagnosis unit 13 diagnoses that the object to be diagnosed has an abnormality or that there is a possibility of an abnormality.

[Operation control device 20]
Next, the operation control device 20 will be described with reference to Figures 9 and 10. Figure 9 is a diagram for explaining an overview of the operation control device 20. After the above-mentioned diagnostic device 10 (10A, 10B) performs an abnormality diagnosis, the operation control device 20 controls the operation of the article conveying equipment 1 in accordance with the measurement data used by the diagnostic device 10 (10A, 10B) for the abnormality diagnosis.

Referring to Figure 10, the operation control of the article conveying equipment 1 by the operation control device 20 will be described. Figure 10 is a diagram for explaining an example of operation control performed by the operation control device 20. Note that, below, the thresholds Th1 and Th3 corresponding to the degree of change in standard deviation and the thresholds Th2 and Th4 corresponding to the degree of change in mean value will be collectively referred to as thresholds ThX. Furthermore, the degree of change in standard deviation and the degree of change in mean value will be collectively referred to simply as the degree of change.

As shown in FIG. 10, the operation control device 20 sets a threshold value Th0 that is smaller than the threshold value ThX used for diagnosis by the diagnosis unit 13, and if the degree of change exceeds ThX, it stops the conveying operation of the item conveying equipment 1, and if the degree of change is between Th0 and ThX, it performs the same conveying operation again, i.e., a retry. Furthermore, if the degree of change is equal to or less than Th0, it continues the operation as it was.

Even if the degree of change is below threshold ThX, if it is close to threshold ThX, there is a possibility that the transport operation is not being performed accurately. Therefore, a threshold Th0 smaller than threshold ThX is set, and if the degree of change is between thresholds Th0 and ThX, a retry is performed. Note that if the degree of change is between thresholds Th0 and ThX, in addition to or without a retry, a request for maintenance may be made to the operator.

Furthermore, retries are not performed for all diagnostic objects 2, but only for diagnostic objects 2 for which a retry is meaningful, such as detectors, reference detection plates, brake pads, running wheels, running rails, etc., which use stop position data as measurement data to perform abnormality diagnosis.

[Example of diagnostic target 2]
Next, examples of the diagnostic object 2 will be described with reference to Figures 11 to 15. The diagnostic object 2 described below can be applied to all of the above-mentioned Embodiments 1 to 4. The diagnostic object 2 includes the guide roller 33, the traveling rail 22, the wheel 231, the motor 25, the reducer 26, the drive shaft rotating part 27, and the chain 30.

Figures 11 to 13 show examples of the running rail 22, wheel 231, motor 25, reducer 26, and drive shaft rotating unit 27.

Figure 11 is a simplified diagram of a friction-driven article transport system 1. Figure 12 is a detailed diagram of area A in Figure 11. Figure 13 is a diagram showing the relationship between the wheels 231 attached to the carriage 23 and the running rails 22 shown in area B in Figure 11.

In the article conveying equipment 1 shown in Figure 11, the load bar 232 attached to the cart 23 is driven by the drive roller 24, causing the cart 23 to move on the running rail 22. As shown in Figure 12, the drive roller 24 rotates when the rotation of the motor 25 is transmitted to the reducer 26, and the drive shaft rotating section 27 rotates as the reducer 26 rotates. As the drive roller 24 rotates, frictional force moves the load bar 232, causing the cart 23 to move. The backup roller 21 is paired with the drive roller 24 and is installed so as to sandwich the load bar 232. By sandwiching the load bar 232 between the drive roller 24 and the backup roller 21, the cart 23 can be moved stably.

Furthermore, as shown in FIG. 12, a sensor 3 may be installed at or near the motor 25. Similarly, a sensor 3 may be installed at or near the reducer 26. The sensor 3 installed at or near the motor 25 may include multiple sensors that detect vibration, current, temperature, and sound, respectively. Furthermore, the sensor 3 installed at or near the reducer 26 may include multiple sensors that detect vibration, temperature, and sound, respectively.

As shown in Figure 13, the wheels 231 of the carriage 23 run on the running rails 22, allowing the carriage 23 to move. The wheels 231 of the carriage 23 and the running rails 22 are in direct contact with each other, and if there is a problem with either of them, noise and other issues associated with the contact will be generated, as described below.

Figure 14 shows an example of a chain 30. Figure 14 is a simplified diagram of a chain-driven item transport equipment 1. In the item transport equipment 1 shown in Figure 14, the chain 30 is driven by the rotation of the drive device 28, which causes the cart 29 to move.

Figure 15 shows an example of a guide roller 33. Figure 15 is a diagram showing the configuration of a trolley 31 in another example of the article conveying equipment 1. Reference numeral 81 in Figure 15 indicates the trolley 31 as viewed from above, and reference numeral 82 indicates the trolley 31 as viewed from the direction of travel or the opposite direction of travel. As shown in reference numeral 81 in Figure 15, in this example, two wall-shaped side guides 32 are provided on both sides of the trolley 31, sandwiching the trolley 31. These side guides 32 form a conveying path for the trolley 31. The trolley 31 is equipped with guide rollers 33 and running wheels 34. The trolley 31 moves using the running wheels 34, and as shown in reference numeral 82 in Figure 15, the guide rollers 33 contact the side guides 32, thereby accurately moving along the conveying path. The running wheels 34 may run directly on the floor surface or on running rails (not shown) provided on the floor surface. The trolley 31 can be stopped by bringing the brake pads (not shown) of the brake into contact with a disk or the like that rotates integrally with the running wheels 34. The stopping position can be controlled by providing a reference detection plate (not shown) on the transport path and a detector (not shown) on the cart 31, which detects the reference detection plate and recognizes the stopping position. The reference detection plate and detector are collectively referred to as the reference sensor. In other words, the reference detection plate and detector can be said to be the reference sensor for the stopping position of the cart 31.

The bogie 31 may be operated individually, or multiple bogies 31 may be operated in a connected state. Furthermore, the bogie 31 may be driven externally by known means, or the bogie 31 itself may be equipped with a drive source.

The diagnostic object 2 is not limited to the guide roller 33; the brake provided on the carriage 31 and the reference sensor for stopping the carriage 31 at a stop position can also be diagnostic object 2. The guide rail of the stacker crane mentioned above can also be diagnostic object 2.

[Example of abnormality diagnosis for each measurement data]
Next, with reference to Fig. 16 to Fig. 22, examples of cases in which the diagnosing unit 13 diagnoses an abnormality for each piece of measurement data will be shown. Fig. 16 to Fig. 18 are diagrams showing an example in which the measurement data is stop position data. Fig. 19 to Fig. 21 are diagrams showing an example in which the measurement data is current data. Fig. 22 is a diagram showing an example in which the measurement data is vibration data or sound data.

[Stop position data]
(Data example 1)
An example of abnormality diagnosis when the measurement data is stop position data will be described with reference to FIG. 16 . FIG. 16 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10A (embodiment 2), where the horizontal axis represents stop position and the vertical axis represents the number of stop position data items over a predetermined period. Graph 1601 in FIG. 16 shows stop position data over a first predetermined period, and graph 1602 shows stop position data over a second predetermined period. The stop position data is the difference between encoder values. The difference between the encoder values is the difference between the encoder value at the correct position previously acquired and the encoder value acquired as measurement data. Graph 1602 is shifted toward the traveling direction from graph 1601. For example, if the average value in graph 1601 is ST1 and the average value in graph 1602 is ST2 (>ST1), the average value change degree calculation unit 122 of the diagnostic device 10A calculates ST2 - ST1 as the average value change degree. The diagnoser 13 then compares the threshold value Th_STa with the average value change rate ST2-ST1, and if Th_STa>ST2-ST1, diagnoses that the diagnostic object 2 is abnormal or has the possibility of being abnormal.

An example of a diagnostic object 2 that can be diagnosed for abnormalities from the degree of change in the average value using stop position data as measurement data is the chain 30. This is because if an abnormality such as elongation occurs in the chain 30, the difference in the encoder values will widen. Another example of a diagnostic object 2 is a reference sensor. If the mounting position of the reference detection plate or detector included in the reference sensor shifts from the correct position, the difference in the encoder values will widen.

(Data example 2)
Another example of abnormality diagnosis when the measurement data is stop position data will be described with reference to FIG. 17 . FIG. 17 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10 (embodiment 1), where the horizontal axis represents the stop position and the vertical axis represents the number of stop position data items over a predetermined period. Graph 1701 in FIG. 17 shows the stop position data over a first predetermined period, and graph 1702 shows the stop position data over a second predetermined period. The stop position data are encoder values. Graph 1702 has a larger standard deviation than graph 1701. For example, if the standard deviation in graph 1701 is S11 and the standard deviation in graph 1702 is S12 (>S11), the standard deviation change degree calculation unit 121 of the diagnostic device 10 calculates S12/S11 as the standard deviation change degree. Then, the diagnosing unit 13 compares the threshold value Th_STb with the standard deviation change rate S12/S11, and if Th_STb>(S12/S11), diagnoses that the diagnostic object 2 is abnormal or has the possibility of being abnormal.

Examples of diagnostic objects 2 that can be diagnosed for abnormalities from the degree of change in standard deviation using stopping position data as measurement data include the wheels 231 and running rails 22. This is because if water, oil, dust, etc. gets in between the wheels 231 and running rails 22, braking becomes unstable and the stopping position becomes inconsistent. Another example of diagnostic object 2 is the brake pads. If the brake pads wear unevenly, braking becomes unstable and the stopping position becomes inconsistent.

(Data example 3)
Referring to FIG. 18 , another example of abnormality diagnosis when the measurement data is stop position data will be described. FIG. 18 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10B (third embodiment), where the horizontal axis represents stop position and the vertical axis represents the number of stop position data items over a predetermined period. Graph 1801 in FIG. 18 shows stop position data over a first predetermined period, and graph 1802 shows stop position data over a second predetermined period. Graph 1802 is shifted toward the traveling direction from graph 1801, and the standard deviation has increased. For example, if the standard deviation in graph 1801 is S41 and the average value is E41, and the standard deviation in graph 1802 is S42 (>S41) and the average value is E42 (>E41), the standard deviation change degree calculation unit 121 of the diagnostic device 10B calculates S42/S41 as the standard deviation change degree. Furthermore, the average value change degree calculation unit 122 calculates |E41-E42| as the average value change degree.

The diagnosis unit 13 then compares the threshold Th_Ex with the standard deviation change rate S42/S41, and compares the threshold Th_Ey with the mean change rate |E41-E42|. If Th_Ex > (S42/S41) and Th_Ey > |E41-E42|, the diagnosis unit 13 diagnoses that the diagnostic object 2 is abnormal or has the possibility of being abnormal.

Examples of diagnostic objects 2 that can be diagnosed for abnormalities using stopping position data as measurement data and the degree of change in standard deviation and mean value include the wheels 231 and running rails 22. This is because if water, oil, dust, etc. gets in between the wheels 231 and running rails 22, braking becomes unstable, the stopping position becomes scattered, and the braking distance increases. Another example of diagnostic objects 2 is the brake pads. If the brake pads wear unevenly, braking becomes unstable, the stopping position becomes scattered, and the braking distance increases.

[Current data]
(Data example 1)
An example of abnormality diagnosis when the measurement data is current data will be described with reference to FIG. 19 . FIG. 19 is a diagram illustrating an example of abnormality diagnosis in the diagnostic device 10A (embodiment 2), where the horizontal axis represents the current value and the vertical axis represents the number of current data points over a predetermined period. Graph 1901 in FIG. 19 shows the current data over a first predetermined period, and graph 1902 shows the current data over a second predetermined period. Graph 1902 is shifted in the direction of increasing current value compared to graph 1901. For example, if the average value in graph 1901 is E1 and the average value in graph 1902 is E2 (>E1), the average value change degree calculation unit 122 of the diagnostic device 10A calculates E2-E1 as the average value change degree. The diagnostic unit 13 then compares the threshold value Th_Ea with the average value change degree E2-E1. If Th_Ea>E2-E1, the diagnostic device 2 is diagnosed as being abnormal or possibly abnormal.

An example of a diagnostic object 2 that can be diagnosed for abnormalities from the degree of change in the average value using current data as measurement data is the reducer 26. This is because if the oil in the reducer 26 deteriorates or leaks, rotational resistance increases, causing an increase in the current supplied to the motor 25. Furthermore, if the resistance increases as the carriage moves, the current supplied to the motor 25 also increases, so any location where an abnormality that could hinder the carriage's movement may occur can be used as a diagnostic object 2.

(Data example 2)
Referring to FIG. 20 , another example of abnormality diagnosis when the measurement data is current data will be described. FIG. 20 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10B (embodiment 3), where the horizontal axis represents the current value and the vertical axis represents the number of current data points over a predetermined period. Graph 2001 in FIG. 20 shows the current data over a first predetermined period, and graph 2002 shows the current data over a second predetermined period. Graph 2002 is shifted in the direction of increasing current value compared to graph 2001, and the standard deviation also increases. For example, if the standard deviation in graph 2001 is S21 and the average value is E11, and the standard deviation in graph 2002 is S22 (>S21) and the average value is E12 (>E11), the standard deviation change degree calculation unit 121 of the diagnostic device 10B calculates S22/S21 as the standard deviation change degree. Furthermore, the average value change degree calculation unit 122 calculates |E11-E12| as the average value change degree.

The diagnosis unit 13 then compares the threshold Th_Eb with the standard deviation change rate S22/S21, and compares the threshold Th_Ec with the mean change rate |E11-E12|. If Th_Eb > (S22/S21) and Th_Ec > |E11-E12|, it diagnoses that the diagnostic object 2 is abnormal or has the possibility of being abnormal.

An example of a diagnostic object 2 that can be diagnosed for abnormalities using current data as measurement data and the degree of change in standard deviation and the degree of change in average value is a lift guide. If a fault occurs in the lift guide that causes operating resistance, the current supplied to the motor 25 increases and becomes unstable.

(Data example 3)
Referring to FIG. 21 , another example of abnormality diagnosis when the measurement data is current data will be described. FIG. 21 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10B (third embodiment), where the horizontal axis represents the current value and the vertical axis represents the number of current data points over a predetermined period. Graph 2101 in FIG. 21 shows the current data over a first predetermined period, and graph 2102 shows the current data over a second predetermined period. Graph 2102 shows a shift in the direction of decreasing current value compared to graph 2101, and the standard deviation increases. For example, if the standard deviation in graph 2101 is S31 and the average value is E21, and the standard deviation in graph 2102 is S32 (>S31) and the average value is E22 (<E21), the standard deviation change degree calculation unit 121 of the diagnostic device 10B calculates S32/S31 as the standard deviation change degree. Furthermore, the average value change degree calculation unit 122 calculates |E21-E22| as the average value change degree.

The diagnosis unit 13 then compares the threshold value Th_Ed with the standard deviation change rate S32/S31, and compares the threshold value Th_Ee with the mean change rate |E21-E22|. If Th_Ed > (S32/S31) and Th_Ee > |E21-E22|, the diagnosis unit 13 diagnoses that the diagnosis object 2 is abnormal or has the possibility of being abnormal.

An example of a diagnostic object 2 for which an abnormality can be diagnosed using current data as measurement data and the degree of change in standard deviation and the degree of change in average value (negative value) is the wheel 231 and the traveling rail 22. If water, oil, dust, etc. gets between the wheel 231 and the traveling rail 22, slippage becomes more likely, reducing the current supplied to the motor 25 and causing instability.

[Sound data, vibration data]
An example of abnormality diagnosis when the measurement data is sound data or vibration data will be described with reference to FIG. 22 . FIG. 22 is a diagram showing an example of abnormality diagnosis in the diagnostic device 10A (embodiment 2), where the horizontal axis represents the volume or vibration amount, and the vertical axis represents the number of pieces of sound data or vibration data in a predetermined period. Graph 2201 in FIG. 22 shows the sound data or vibration data in a first predetermined period, and graph 2202 shows the sound data or vibration data in a second predetermined period. Graph 2202 is shifted from graph 2201 in the direction of increasing volume or vibration amount. For example, if the average value in graph 2201 is FS1 and the average value in graph 2202 is FS2 (>FS1), the average value change degree calculation unit 122 of the diagnostic device 10A calculates |FS1-FS2| as the average value change degree. The diagnoser 13 then compares the threshold value Th_FS with the degree of change in the average value |FS1-FS2|, and if Th_FS>|FS1-FS2|, diagnoses that the diagnostic object 2 is abnormal or has the possibility of being abnormal.

Examples of diagnostic objects 2 that can be diagnosed for abnormalities based on the degree of change in the average value using sound data or vibration data as measurement data include the reducer 26 and the drive shaft rotating part 27. This is because if a fault occurs in the bearings of the reducer 26 or the drive shaft rotating part 27, the sound and vibration generated from that location will increase.

Furthermore, sound data or vibration data can be used as measurement data, and abnormalities can be diagnosed from the degree of change in standard deviation and the degree of change in mean value. In this case, the degree of change in standard deviation and the degree of change in mean value show the same trends as when the measurement data is current data, as explained above with reference to FIG. 20. Therefore, the diagnosis unit 13 can diagnose that the diagnosis object 2 is abnormal or has the possibility of abnormality, using the same method as when the measurement data is current data.

An example of a diagnostic object 2 that can be diagnosed for abnormalities using sound data or vibration data as measurement data and the degree of change in standard deviation and mean value is an elevator guide. If an obstacle that causes operational resistance occurs in the elevator guide, the sound or vibration will increase and the elevator guide will become unstable.

[Processing flow]
The processing flow in the diagnostic device 10 will be described with reference to Fig. 23. Fig. 23 is a flowchart showing an example of the processing flow in the diagnostic device 10. As shown in Fig. 23, the acquisition unit 11 of the diagnostic device 10 acquires measurement data from a sensor 3 installed on or near the item conveying facility 1 (S101, acquisition step). The measurement data is acquired repeatedly.

The diagnostic device 10 also performs the following processes (S102 to S106) in parallel with acquiring the measurement data (S101). First, the calculation unit 12 determines whether a predetermined period has elapsed (S102). If the predetermined period has elapsed (YES in S102), the calculation unit 12 calculates the standard deviation of the measurement data acquired during that predetermined period (S103). Next, the calculation unit 12 compares the calculated standard deviation with the standard deviation during the previous predetermined period, and calculates the degree of change in standard deviation (S104, calculation step).

Then, the diagnosis unit 13 determines whether the degree of change in standard deviation calculated by the calculation unit 12 satisfies a predetermined condition (S105). If the condition is satisfied (YES in S105), the diagnosis unit 13 diagnoses that there is an abnormality or there is a possibility of an abnormality in the diagnosis object 2 (S106, diagnosis step). Then, the process returns to step S102. On the other hand, if the degree of change in standard deviation does not satisfy the condition (NO in S105), the process returns to step S102.

Next, the processing flow in diagnostic device 10A will be described with reference to Figure 24. Figure 24 is a flowchart showing an example of the processing flow in diagnostic device 10A.

As shown in FIG. 24, first, the acquisition unit 11 of the diagnostic device 10A acquires measurement data from a sensor 3 installed on or near the item conveying equipment 1 (S201, acquisition step). The measurement data is acquired repeatedly.

In addition, the diagnostic device 10A performs the following processes (S202 to S206) in parallel with acquiring the measurement data (S201). First, the calculation unit 12A determines whether a predetermined period has elapsed (S202). If the predetermined period has elapsed (YES in S202), the calculation unit 12A calculates the average value of the measurement data acquired during that predetermined period (S203). The calculation unit 12A then compares the calculated average value with the average value for the previous predetermined period and calculates the degree of change in the average value (S204, calculation step).

Then, the diagnosis unit 13 determines whether the degree of change in the average value calculated by the calculation unit 12A satisfies a predetermined condition (S205). If the condition is satisfied (YES in S205), the diagnosis unit 13 diagnoses that there is an abnormality or there is a possibility of an abnormality in the diagnosis object 2 (S206, diagnosis step). Then, the process returns to step S202. On the other hand, if the degree of change in the average value does not satisfy the condition (NO in S205), the process returns to step S202.

As described above, the diagnostic device 10 according to this embodiment includes an acquisition unit 11 that acquires one or more types of measurement data used to diagnose an abnormality in a diagnostic object 2 included in an item conveying facility 1; a calculation unit 12 that calculates a standard deviation change rate, which is the rate of change between a first standard deviation of multiple measurement data of the same type measured during a predetermined conveying operation in a first predetermined period and a second standard deviation of multiple measurement data of the same type as the first standard deviation measured during the predetermined conveying operation in a second predetermined period after the first predetermined period; and a diagnostic unit 13 that performs an abnormality diagnosis on the diagnostic object 2 based on the calculated standard deviation change rate.

The above configuration allows for abnormality diagnosis of the item conveying equipment 1 based on changes in the standard deviation of the measurement data. This is therefore useful for diagnosing abnormalities in measurement objects where the greater the tendency for abnormality, the greater the variation in measurement data. In this case, abnormality diagnosis of the object to be diagnosed can be performed accurately with a simple configuration.

The diagnostic device 10A according to this embodiment also includes an acquisition unit 11 that acquires measurement data indicating at least one of the current value, vibration amount, sound volume, and stop position of the transport vehicle to be used in diagnosing an abnormality in a diagnostic object included in the item transport equipment 1; a calculation unit 12A that calculates a first average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period, and an average value change rate that is the rate of change in a second average value of a plurality of measurement data of the same type as the first measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period; and a diagnostic unit 13 that performs an abnormality diagnosis on the diagnostic object according to the calculated average value change rate.

This configuration allows for abnormality diagnosis of item transport equipment based on changes in the average value of the measurement data. This is therefore useful for diagnosing abnormalities in measurement objects where the average value of the measurement data changes as the tendency for abnormality becomes stronger.

[Software implementation example]
The functions of the diagnostic devices 10, 10A, and 10B (hereinafter referred to as "devices") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block (acquisition unit 11, calculation unit 12, diagnosis unit 13) of the device.

In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The functions described in each of the above embodiments are realized by executing the program using this control device and storage device.

The above program may be stored non-transitory on one or more computer-readable storage media. These storage media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

Furthermore, some or all of the functions of each of the above control blocks can be realized by logic circuits. For example, integrated circuits incorporating logic circuits that function as each of the above control blocks are also included in the scope of the present invention. In addition, the functions of each of the above control blocks can also be realized by, for example, a quantum computer.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

〔summary〕
A diagnostic device according to a first aspect of the present invention includes an acquisition unit that acquires one or more types of measurement data used for diagnosing an abnormality in a diagnostic object included in an item conveying facility; a calculation unit that calculates a standard deviation change degree, which is the degree of change between a first standard deviation of a plurality of measurement data of the same type measured during a predetermined conveying operation in a first predetermined period and a second standard deviation of a plurality of measurement data of the same type as the first standard deviation measured during the predetermined conveying operation in a second predetermined period after the first predetermined period; and a diagnostic unit that performs an abnormality diagnosis of the diagnostic object according to the calculated standard deviation change degree.

A diagnostic device according to Aspect 2 of the present invention is the same as Aspect 1, wherein the calculation unit calculates the ratio of the second standard deviation to the first standard deviation as the degree of change in standard deviation.

A diagnostic device according to aspect 3 of the present invention may be configured as in aspect 1 or 2, wherein the diagnostic unit diagnoses that the object to be diagnosed is abnormal or that there is a possibility of an abnormality in the object to be diagnosed when the degree of change in standard deviation satisfies a predetermined condition.

A diagnostic device according to Aspect 4 of the present invention is any of Aspects 1 to 3, wherein the calculation unit further calculates a degree of change in average value, which is the degree of change between a first average value of a plurality of measurement data of the same type measured during the predetermined conveying operation in the first predetermined period and a second average value of a plurality of measurement data of the same type measured during the predetermined conveying operation in the second predetermined period, and the diagnostic unit may diagnose an abnormality in the item conveying equipment based on the degree of change in standard deviation and the degree of change in average value.

A diagnostic device according to Aspect 5 of the present invention is similar to Aspect 4, but the calculation unit may calculate the difference between the first average value and the second average value as the degree of change in the average value.

A diagnostic device according to aspect 6 of the present invention may be configured as in aspect 4 or 5, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that the diagnostic object may be abnormal when a combination of the standard deviation change rate and the mean value change rate satisfies a predetermined condition.

A diagnostic device according to aspect 7 of the present invention is any of aspects 1 to 6, wherein the measurement data may include at least one of the following types: data indicating vibrations of the diagnostic object or a position near the diagnostic object; data indicating the current supplied to the diagnostic object; data indicating sound collected near the diagnostic object; and data indicating the stopping position of a transport vehicle.

A diagnostic device according to aspect 8 of the present invention includes an acquisition unit that acquires measurement data indicating at least one of a current value, vibration amount, sound volume, and stop position of a transport vehicle to be used for diagnosing an abnormality in a diagnostic object included in an item transport facility; a calculation unit that calculates a first average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period, and a degree of change in average value, which is the degree of change in a second average value of a plurality of measurement data of the same type as the first measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period; and a diagnosis unit that performs an abnormality diagnosis on the diagnostic object based on the calculated degree of change in average value.

A diagnostic device according to Aspect 9 of the present invention is the same as Aspect 8, wherein the calculation unit calculates the difference between the first average value and the second average value as the degree of change in the average value.

A diagnostic device according to aspect 10 of the present invention may be configured as in aspect 8 or 9, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that the diagnostic object may be abnormal when the degree of change in the average value satisfies a predetermined condition.

A diagnostic device according to Aspect 11 of the present invention is any of Aspects 8 to 10, wherein the calculation unit further calculates a standard deviation change rate, which is the rate of change between a first standard deviation of a plurality of measurement data of the same type measured during the predetermined conveying operation in the first predetermined period and a second standard deviation of a plurality of measurement data of the same type as the first standard deviation measured during the predetermined conveying operation in the second predetermined period, and the diagnostic unit may perform an abnormality diagnosis of the item conveying equipment based on the mean value change rate and the standard deviation change rate.

A diagnostic device according to Aspect 12 of the present invention is the same as Aspect 11, except that the calculation unit calculates the ratio of the second standard deviation to the first standard deviation as the degree of change in standard deviation.

A diagnostic device according to aspect 13 of the present invention may be configured as in aspect 11 or 12, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that the diagnostic object may be abnormal when a combination of the mean value change rate and the standard deviation change rate satisfies a predetermined condition.

A diagnostic device according to aspect 14 of the present invention is any of aspects 1 to 13, wherein the object to be diagnosed may be any of a motor, a reducer, a drive shaft rotating part, a running rail, a guide rail, a guide roller, a chain, a wheel of a transport vehicle, a brake of a transport vehicle, and a reference sensor for a stop position of the transport vehicle.

The operation control device according to aspect 15 of the present invention controls the operation of the item conveying equipment in accordance with the values of the measurement data used in the abnormality diagnosis performed by the diagnostic device according to any one of aspects 1 to 14.

A diagnostic method according to aspect 16 of the present invention includes an acquisition step of acquiring one or more types of measurement data used for diagnosing an abnormality in a diagnostic object included in an item conveying facility; a calculation step of calculating a standard deviation change rate, which is the rate of change between a first standard deviation of multiple measurement data of the same type measured during a predetermined conveying operation in a first predetermined period and a second standard deviation of multiple measurement data of the same type as the acquired measurement data measured during the predetermined conveying operation in a second predetermined period after the first predetermined period; and a diagnosis step of diagnosing an abnormality in the diagnostic object based on the calculated standard deviation change rate.

A diagnostic method according to aspect 17 of the present invention includes an acquisition step of acquiring measurement data indicating at least one of the current value, vibration amount, sound volume, and stop position of the transport vehicle to be used for diagnosing an abnormality in a diagnostic object included in an item transport facility; a calculation step of calculating a degree of change in the average value, which is the degree of change between a first average value of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period and a second average value of a plurality of measurement data of the same type as the measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period; and a diagnosis step of diagnosing an abnormality in the diagnostic object based on the calculated degree of change in the average value.

REFERENCE SIGNS LIST 1 Item transport equipment 2 Diagnosis target 3 Sensor 10, 10A, 10B Diagnosis device 11 Acquisition unit 12, 12A, 12B Calculation unit 121 Standard deviation change degree calculation unit 122 Mean value change degree calculation unit 13 Diagnosis unit 21 Backup roller 22 Traveling rail 23 Cart 231 Wheel 232 Load bar 24 Drive roller 25 Motor 26 Reducer 27 Drive shaft rotating unit 28 Driving device 29 Cart 30 Chain 31 Cart 32 Side guide 33 Guide roller 34 Traveling wheel

Claims (10)

an acquisition unit that acquires one or more types of measurement data used for diagnosing an abnormality in a diagnosis target included in the item transport equipment;
a calculation unit that calculates a standard deviation change degree, which is a degree of change between a first standard deviation of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period and a second standard deviation of a plurality of measurement data of the same type as the first standard deviation of the plurality of measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period;
a diagnosis unit that performs an abnormality diagnosis on the object to be diagnosed in accordance with the calculated degree of change in standard deviation,
The calculation unit further
calculating an average value change rate, which is a rate of change between a first average value of a plurality of measurement data of the same type measured during the predetermined transport operation in the first predetermined period and a second average value of a plurality of measurement data of the same type as the first average value measured during the predetermined transport operation in the second predetermined period;
The diagnostic unit performs an abnormality diagnosis on the object to be diagnosed in accordance with the degree of change in the standard deviation and the degree of change in the mean value. The diagnostic device of claim 1, wherein the calculation unit calculates the ratio of the second standard deviation to the first standard deviation as the degree of change in standard deviation. The diagnostic device of claim 1 or 2, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that there is a possibility of abnormality in the diagnostic object when the degree of change in standard deviation satisfies a predetermined condition. The diagnostic device of claim 1, wherein the calculation unit calculates the difference between the first average value and the second average value as the degree of change in the average value. The diagnostic device of claim 1 or 4, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that there is a possibility of abnormality in the diagnostic object when the combination of the standard deviation change rate and the mean value change rate satisfies a predetermined condition. The measurement data is
data indicating vibrations at or near the diagnostic target;
data indicating a current supplied to the diagnostic object;
data indicating sounds collected in the vicinity of the diagnosis target;
Data indicating the stopping position of the transport vehicle,
The diagnostic device according to claim 1 , comprising at least one of the following types: The diagnostic device of claim 1, wherein the diagnostic unit diagnoses that the diagnostic object is abnormal or that there is a possibility of abnormality in the diagnostic object when the degree of change in the average value satisfies a predetermined condition. The diagnostic device of claim 1, wherein the object to be diagnosed is any one of a motor, a reducer, a drive shaft rotating part, a running rail, a guide rail, a guide roller, a chain, a wheel of a transport vehicle, a brake of a transport vehicle, and a reference sensor for a transport vehicle stopping position. The diagnostic device according to claim 1 ;
an operation control device that controls operation of the article conveying facility in accordance with values of the measurement data used in the abnormality diagnosis performed by the diagnosis device;
A system including: an acquisition step of acquiring one or more types of measurement data used for diagnosing an abnormality in a diagnosis target included in the item transport equipment;
a calculation step of calculating a standard deviation change degree, which is a degree of change between a first standard deviation of a plurality of measurement data of the same type measured during a predetermined transport operation in a first predetermined period and a second standard deviation of a plurality of measurement data of the same type as the first standard deviation of the plurality of measurement data measured during the predetermined transport operation in a second predetermined period after the first predetermined period;
calculating an average value change rate, which is a rate of change between a first average value of a plurality of measurement data of the same type measured during the predetermined transport operation in the first predetermined period and a second average value of a plurality of measurement data of the same type as the first average value measured during the predetermined transport operation in the second predetermined period;
a diagnosing step of diagnosing an abnormality of the object to be diagnosed in accordance with the calculated degree of change in standard deviation and the degree of change in average value.