WO2020044993A1

WO2020044993A1 - Information processing device and information processing method

Info

Publication number: WO2020044993A1
Application number: PCT/JP2019/031053
Authority: WO
Inventors: 仁友定; 克行木村
Original assignee: オムロン株式会社
Priority date: 2018-08-31
Filing date: 2019-08-07
Publication date: 2020-03-05
Also published as: JP2020035372A

Abstract

The present invention stably detects an abnormality of a movable device in which a motor is used. A PLC (50) is provided with an abnormality detection unit (56) that detects an abnormality of a movable device (10) by comparing, with a threshold value (T1), a difference between the maximum value and the minimum value in averages of torque values of a motor (30).

Description

Information processing apparatus and information processing method

The present invention relates to an information processing apparatus and an information processing method for detecting an abnormality in a movable device using the motor based on an output value of the motor.

装置 A device that monitors an abnormality of a movable device using the motor based on an output value of the motor is used. In the tool abnormality detection method of Patent Literature 1, a power value that periodically changes with driving of a motor is sequentially acquired, a variance value of a time that changes by one or more cycles is set as a first power variance value, and A variance value of the first power variance value is set as a second power variance value, and a ratio between the latest second power variance value and a predetermined number of second power variance values is sequentially obtained. If it exceeds the predetermined number of times, it is determined that an abnormality has occurred in the power value.

Japanese Unexamined Patent Publication “JP 2016-40072”

可動 Movable devices using motors often fail gradually due to wear due to operation or looseness due to vibration. According to the tool abnormality detection method of Patent Document 1, the ratio of the latest second power variance value to the second power variance value before a predetermined number (that is, a predetermined time before) is compared with a threshold value, and the power value The presence or absence of an abnormality is determined. Therefore, when the performance of the movable device gradually decreases due to the gradual failure of the movable device, the abnormality of the movable device may not be noticed.

An object of one embodiment of the present invention is to stably detect an abnormality of a movable device using a motor.

The present invention employs the following configuration in order to solve the above-described problems.

That is, an information processing device according to one aspect of the present invention is an information processing device that detects an abnormality of a movable device that performs a predetermined operation by rotation of a motor, and acquires an output value associated with rotation of the motor with time. An acquisition unit, a feature amount calculation unit that calculates a feature amount for each of the predetermined operations from the output value obtained by the acquisition unit, and a distribution index that is an index indicating a width of the distribution of the feature amount in a first predetermined period. A distribution index calculation unit for calculating; and an abnormality detection unit for detecting an abnormality of the movable device by comparing the distribution index with a threshold.

An information processing method according to one aspect of the present invention is an information processing method for an information processing apparatus that detects an abnormality of a movable device that performs a predetermined operation by rotation of a motor, and outputs an output value accompanying rotation of the motor over time. An acquiring step of acquiring, a feature amount calculating step of calculating a feature amount for each of the predetermined operations from the output value obtained in the acquiring step, and a distribution which is an index indicating a width of a distribution of the feature amount in a first predetermined period. A distribution index calculating step of calculating an index; and an abnormality detecting step of detecting an abnormality of the movable device by comparing the distribution index with a threshold.

According to an embodiment of the present disclosure, it is possible to stably detect an abnormality of a movable device using a motor.

FIG. 2 is a diagram illustrating an example of a functional block of the PLC according to the first embodiment. FIG. 3 is a diagram schematically illustrating an example of an application scene of the PLC according to the first embodiment. FIG. 4 is a diagram illustrating a temporal change of a torque value of a motor when the movable device according to the first embodiment performs a predetermined operation. FIG. 4 is a diagram illustrating a temporal change of an average value of torque values for each predetermined operation of the movable device according to the first embodiment. FIG. 4 is a diagram illustrating a temporal change in PP of an average value of torque values for each predetermined period according to the first embodiment. 5 is a flowchart illustrating a flow of calculating a threshold value in the PLC according to the first embodiment. 5 is a flowchart illustrating a flow of detecting an abnormality of a movable device in the PLC according to the first embodiment.

Hereinafter, an embodiment according to one aspect of the present invention (hereinafter, also referred to as “the present embodiment”) will be described with reference to the drawings.

§1 Application Example An example of a scene to which the present invention is applied will be described with reference to FIG. FIG. 2 is a diagram schematically illustrating an example of an application scene of a PLC (programmable logic controller) 50 according to the first embodiment. The PLC 50 according to the present embodiment is an example of the information processing apparatus of the present invention that detects an abnormality of the movable device 10 including the movable mechanism (the movable portion 13 and the ball screw 12) that performs a predetermined operation by the rotation of the motor 30 in real time. .

The movable system 1 includes the movable device 10, the motor 30, a driver 40 that controls the driving of the motor 30, and a PLC 50. The movable system 1 is, for example, a production facility used in a product manufacturing factory. The movable device 10 is a device that performs a predetermined operation, for example, by transmitting rotation of a motor 30 connected via the coupling 20. The movable device 10 may be any device that performs a predetermined operation when the rotation of the motor 30 is transmitted, and may be, for example, various devices such as a transport device used in a manufacturing plant, or used in a device other than the manufacturing plant. Devices such as various types of robots.

The PLC 50 may be any information processing device that acquires an output value associated with the rotation of the motor 30 over time and detects an abnormality of the movable device 10 based on the output value. As the information processing device, for example, a server And so on. For example, the PLC 50 detects an abnormality based on the variance of the average value of the torque in a predetermined operation in a predetermined period.

§2 Configuration Example A configuration example of the movable system 1 according to the first embodiment will be described with reference to FIG. The movable device 10 includes, for example, a base 11 having a linear guide, a ball screw 12, and a movable portion 13. The ball screw 12 is mounted on the base 11 so as to be parallel to the long axis direction of the base 11. The movable section 13 is mounted on the ball screw 12. One end of the ball screw 12 is connected to an output shaft of the motor 30 via a coupling.

(4) The rotation of the output shaft of the motor 30 (simply called the rotation of the motor 30) causes the ball screw 12 to rotate. Then, as the ball screw 12 rotates, the movable portion 13 moves (ie, moves) on the ball screw 12 and along the ball screw 12 as indicated by an arrow A1.

In the movable device 10, for example, the movable portion 13 moves from the vicinity of one end (the end closer to the motor 30) of the ball screw 12 to the vicinity of the other end (the end farther from the motor 30) (outbound path). The movable portion 13 moves from the vicinity of the other end of the ball screw 12 to the vicinity of one end (return direction) of the ball screw 12 as a one-cycle operation. . The predetermined operation in the movable device 10 may be the one-cycle operation or a plurality of cycles including the one-cycle operation a plurality of times.

The motor 30 may include an encoder. When the motor 30 includes an encoder (that is, when the motor 30 includes the encoder), for example, the encoder included in the motor 30 outputs information indicating the rotation direction and the rotation angle to the driver 40 over time as a pulse signal. I do.

Alternatively, the encoder may be externally provided separately from the motor 30. When the encoder is provided separately from the motor 30, for example, the encoder is attached to the other end of the ball screw 12 (the end farther from the motor 30) via a coupling. Then, the encoder outputs information indicating the rotation direction and the rotation angle of the ball screw 12 to the PLC 50 over time as a pulse signal.

An encoder built in the motor 30 or an external encoder provided separately from the motor 30 is in contact with the rotating portion and a sensing portion that senses the rotation direction and the rotation angle of the rotating portion. It may be a contact type encoder or a non-contact type encoder in which a rotating part and a sensing part are not in contact with each other.

For example, the driver 40 outputs appropriate values of current and voltage to the motor 30 based on an instruction from the PLC 50 so that the number of rotations of the motor 30 per unit time becomes a predetermined value. Thus, the driver 40 controls the rotation of the motor 30. For example, the driver 40 calculates a torque value, which is an output value associated with the rotation of the motor 30, with time based on the current value and the voltage value of the current and the voltage output to the motor 30, and sequentially outputs the torque value to the PLC 50. .

Alternatively, when an encoder is built in the motor 30, the driver 40 sequentially calculates an angular velocity value, which is an output value accompanying rotation of the motor 30, based on a pulse signal acquired from the motor 30, and sequentially , PLC50.

In addition, for example, an acceleration sensor is attached to the movable unit 13, the acceleration sensor acquires the acceleration data of the movable unit 13 that moves with the rotation of the motor 30 over time, and the acceleration sensor determines the acceleration value indicating the acceleration value. The data may be sequentially output to the PLC 50 as an output value accompanying the rotation of the motor 30. The torque value, the angular velocity value, and the acceleration value as described above are merely examples of the output value associated with the rotation of the motor 30. The output value associated with the rotation of the motor 30 is obtained with the rotation of the motor 30. Any value is acceptable.

FIG. 1 is a diagram illustrating an example of functional blocks of the PLC 50 according to the first embodiment. The PLC 50 includes an acquisition unit 51, a threshold calculation unit 52, a storage unit 53, a feature calculation unit 54, a distribution index calculation unit 55, and an abnormality detection unit 56. Hereinafter, each unit included in the PLC 50 will be described.

FIG. 3 is a diagram illustrating a temporal change in the torque value of the motor 30 when the movable device 10 according to the first embodiment performs a predetermined operation.

The acquisition unit 51 acquires a torque value over time as an output value accompanying the rotation of the motor 30 from the driver 40, for example, as shown in a series 1 in FIG. FIG. 3 illustrates an example in which the acquisition unit 51 also acquires information indicating the rotation direction of the motor 30 from the driver 40 with time, as shown in the series 2. The +1 period (periods B1 and B3) in the series 2 is a period during which the driver 40 instructs the motor 30 to rotate clockwise. When the motor 30 rotates clockwise, the movable section 13 moves in the forward direction. The period -1 (periods B2 and B4) is a period during which the driver 40 instructs the motor 30 to rotate in the counterclockwise direction. As the motor 30 rotates counterclockwise, the movable portion 13 moves in the backward direction. The period of 0 is a period during which the rotation instruction of the driver 40 to the motor 30 is stopped, and a period during which the movable unit 13 is about to stop (that is, a period during which the torque value of the motor 30 is decreasing) or a period during which it is stopped Is represented. That is, each of the period B1, the period B2, the period B3, the period B4,... Is an operation period of one cycle in the movable device 10. In the present embodiment, the predetermined operation of the movable device 10 is described as one cycle of operation, but may be a plurality of cycles of operation.

The acquisition unit 51 may acquire the angular velocity value of the motor 30 from the driver 40 over time as an output value associated with the rotation of the motor 30. Further, for example, when the acceleration sensor is attached to the movable unit 13, the acquisition unit 51 may acquire acceleration data indicating an acceleration value from the acceleration sensor as an output value accompanying rotation of the motor 30.

The threshold calculating unit 52 calculates a threshold from the output value of the motor 30 as a criterion for the abnormality detecting unit 56 to detect abnormality of the movable device 10 and stores the threshold in the storage unit 53. The threshold calculator 52 calculates a threshold from the output value obtained by the obtaining unit 51 in a second predetermined period that is a fixed period. The second predetermined period is not a period in which the abnormality detection unit 56 moves according to the progress of the abnormality detection processing such as the second power dispersion value described in Patent Literature 1, but the abnormality detection unit 56 uses the movable device 10 based on the output value of the motor 30. This is a fixed period irrespective of the progress of the process of detecting the abnormality of the image. For example, the second predetermined period may be a period immediately after activating the PLC 50, a predetermined period from a preset time in a day, or the like. The second predetermined period is preferably a period in which no abnormality of the movable device 10 has been detected by the PLC 50 from the output value of the motor 30 in the past.

閾値 Since the threshold value calculation unit 52 automatically calculates and sets the threshold value, the work efficiency of the user can be increased as compared with the case where the user inputs the threshold value. Further, since the threshold value calculation unit 52 calculates the threshold value from the output value associated with the actual rotation of the motor 30, the threshold value calculation unit 52 can set the threshold value according to the operation state of the motor 30. According to this, the detection accuracy of the abnormality of the movable device 10 can be improved. The threshold value may be directly input to the PLC 50 by the user. An example of a specific process in which the threshold calculator 52 calculates the threshold will be described later with reference to FIG.

FIG. 4 is a diagram illustrating a temporal change in the average value of the torque value for each predetermined operation of the movable device 10 according to the first embodiment.

The feature value calculation unit 54 calculates a feature value for each predetermined operation of the movable device 10 from the output value acquired by the acquisition unit 51. For example, based on the torque value acquired by the acquisition unit 51, the feature amount calculation unit 54 sets each of the periods B1, B2, B3, B4,... Shown in FIG. Is calculated as a feature value. In the example shown in FIG. 4, the average value of the torque values for each of the periods B1, B2, B3, B4,. ing.

The feature value calculated by the feature value calculation unit 54 may be other than the average value, and examples thereof include a variance, a median value, and a difference between the maximum value and the minimum value. Further, the feature amount calculation unit 54 calculates the feature amount not for each of the periods B1, B2, B3, B4,... Shown in FIG. 3 but for a plurality of periods (for example, for each of the periods B1 + B2, B3 + B4). May be. Note that the average value, the variance, the median value, the difference between the maximum value and the minimum value, and the like described above are merely examples of the feature amount calculated by the feature amount calculation unit 54, and the feature amount calculation unit 54 The characteristic value to be calculated may be any value that can be calculated from the output value of the motor 30.

FIG. 5 is a diagram showing a temporal change in PP of the average value of the torque value for each predetermined period according to the first embodiment.

The distribution index calculation unit 55 calculates a distribution index, which is an index indicating the width of the distribution of the feature amount in each first predetermined period, from the feature amount calculated by the feature amount calculation unit 54. For example, the distribution index calculator 55 calculates a difference (PP: PeakPto Peak) between the average calculated by the feature calculator 54 and the maximum value and the minimum value indicating the width of the distribution of the average in the first predetermined period. ) Is calculated as a distribution index. In the example shown in FIG. 5, the difference between the maximum value P1 and the minimum value P2 of the average value for each of the average values C10, C2, and C3 representing the first predetermined period in FIG. The state of the temporal change of the difference (PP) between the maximum value and the minimum value plotted in the axial direction is shown.

The first predetermined period can be set arbitrarily. For example, at least some of the periods C1, C2, and C3 may overlap, or a period in which the periods C1, C2, and C3 are not continuous (that is, the periods C1 and C2 are (A separated period, a period in which the period C2 and the period C3 are separated), and the like.

As described above, when calculating the difference between the maximum value and the minimum value of the average value in the first predetermined period as the distribution index, the distribution index calculation unit 55 calculates the difference between the maximum value and the minimum value of the average value in the first predetermined period. Should be stored in memory. Therefore, as compared with the case where the variance is calculated as the distribution index, the memory area required for the calculation can be reduced, and as a result, the load required for the calculation can be reduced.

The distribution index calculated by the distribution index calculation unit 55 may be other than the difference between the maximum value and the minimum value, and may be, for example, variance, skewness of a normal distribution, or the like. In this way, it is also possible to stably detect the abnormality of the movable device 10 from the output value of the motor 30. The difference between the maximum value and the minimum value, the variance, the skewness of the normal distribution, and the like described above are merely examples of the distribution index calculated by the distribution index calculation unit 55, and the distribution calculated by the distribution index calculation unit 55. The index may be any index that indicates the width of the distribution of the feature amount.

The abnormality detection unit 56 refers to the threshold value T1 (for example, 0.46) stored in the storage unit 53, and refers to the threshold value T1 and the feature amount (here, (The difference between the maximum value and the minimum value), and when the feature amount exceeds the threshold value T1, it is detected in real time that an abnormality has occurred in the movable device 10.

(4) When detecting that the feature amount has become equal to or larger than the threshold value T1 (that is, an abnormality has occurred in the movable device 10), the abnormality detection unit 56 performs a process of notifying the user of the fact. The process of notifying the user includes various methods for notifying the user. Examples of the process of notifying the user include displaying a screen for notifying the user on the display when the movable system 1 includes a display, and outputting a sound for notifying the user when the movable system 1 includes a speaker. For example, to output the data to Alternatively, as a process of notifying the user, for example, a process of notifying the user by sending an e-mail to an address designated by the user, or a process of turning on or blinking the lamp when the movable system 1 includes a lamp And the like.

When detecting that the characteristic amount has become equal to or greater than the threshold value T1, the abnormality detection unit 56 executes a process of notifying the user of the fact and / or a process of stopping the rotation of the motor 30 for the driver 40. Is also good. Thus, when an abnormality occurs in the movable device 10, the movable device 10 can be prevented from being broken.

出力 Here, the output value such as the torque value of the motor tends to change due to disturbance such as temperature. For this reason, it is difficult to detect a sign of failure of the movable device only by comparing the output value such as the torque value of the motor with the threshold value.

Therefore, according to the PLC 50, the distribution index (for example, the difference (PP) between the maximum value and the minimum value of the average value) itself, which is an index indicating the width of the distribution of the feature amount (for example, the average value) in each first predetermined period, itself. Is successively compared with the threshold by the abnormality detection unit 56, thereby detecting an abnormality of the movable device 10 in real time. Therefore, even if disturbance such as temperature is applied to the motor 30, an abnormality of the movable device 10 can be detected stably. In addition, unlike Patent Document 1, even if the movable device 10 gradually breaks down due to various causes such as wear due to the operation of the movable device 10 or looseness due to vibration, the abnormality of the movable device 10 is detected. can do. Thereby, the abnormality of the movable device 10 can be stably detected.

FIG. 6 is a flowchart illustrating a flow of calculating the threshold T1 in the PLC 50 according to the first embodiment. The threshold value T1 is calculated by the threshold value calculation unit 52. The threshold calculator 52 calculates the threshold T1 and stores the threshold T1 in the storage 53 in advance before the feature calculator 54, the distribution index calculator 55, and the abnormality detector 56 perform their respective processes. Note that the flowchart illustrated in FIG. 6 is an example of a flow in which the threshold value calculation unit 52 calculates the threshold value T1. The threshold calculator 52 may calculate the threshold T1 by another method.

The acquisition unit 51 acquires the torque value over time in the second predetermined period from the driver 40 (Step S1). Next, the threshold calculator 52 calculates the average value of the torque values in the second predetermined period obtained by the obtaining unit 51 (step S2), and further, the standard deviation of the torque values in the second predetermined period obtained by the obtaining unit 51. Is calculated (step S3). Then, the threshold value calculation unit 52 calculates the threshold value T1 by multiplying the standard deviation calculated in step S3 by three and adding the average value calculated in step S2 (step S4). The threshold value calculation unit 52 stores the threshold value T1 obtained in step S4 in the storage unit 53.

FIG. 7 is a flowchart illustrating a flow of detecting an abnormality of the movable device 10 in the PLC 50 according to the first embodiment. The obtaining unit 51 obtains a torque value over time from the driver 40 (Step S11). Next, the characteristic amount calculating unit 54 calculates an average value for each predetermined operation of the movable device 10 with time from the torque value acquired by the acquiring unit 51 (Step S12). Then, the distribution index calculation unit 55 calculates a difference (PP) between the maximum value and the minimum value of the average value for each first predetermined period with time from the average value calculated by the feature value calculation unit 54 ( Step S13). Next, the abnormality detection unit 56 successively compares the difference (PP) between the maximum value and the minimum value of the average value calculated by the distribution index calculation unit 55 with the threshold value T1 stored in the storage unit 53. Then, it is determined whether or not the difference (PP) between the maximum value and the minimum value of the average value is equal to or larger than the threshold value T1 (step S14).

In step S14, when the abnormality detection unit 56 determines that the difference (PP) between the maximum value and the minimum value of the average value is equal to or larger than the threshold value T1 (YES in step S14), the abnormality detection unit 56 performs a notification process to the user (step S14). Step S15).

[Example of software implementation]
The control blocks of the PLC 50 (in particular, the threshold value calculation unit 52, the feature value calculation unit 54, the distribution index calculation unit 55, and the abnormality detection unit 56) are realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like. Or may be realized by software.

In the latter case, the PLC 50 includes a computer that executes instructions of a program that is software for realizing each function. This computer includes, for example, one or more processors and a computer-readable recording medium storing the program. Then, in the computer, the object of the present invention is achieved when the processor reads the program from the recording medium and executes the program. As the processor, for example, a CPU (Central Processing Unit) can be used. Examples of the recording medium include “temporary tangible media” such as ROM (Read Only Memory), tapes, disks, cards, semiconductor memories, and programmable logic circuits. Further, a RAM (Random Access Memory) for expanding the program may be further provided. Further, the program may be supplied to the computer via an arbitrary transmission medium (a communication network, a broadcast wave, or the like) capable of transmitting the program. Note that one embodiment of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the above-described program is embodied by electronic transmission.

(Appendix)
An information processing device according to one aspect of the present invention is an information processing device that detects an abnormality of a movable device that performs a predetermined operation by rotation of a motor, and acquires an output value associated with rotation of the motor with time. And a feature value calculation unit that calculates a feature value for each of the predetermined operations from the output value obtained by the obtaining unit, and calculates a distribution index that is an index indicating a width of the distribution of the feature value in a first predetermined period. A distribution index calculation unit; and an abnormality detection unit that detects an abnormality of the movable device by comparing the distribution index with a threshold.

According to the above configuration, the abnormality of the movable device is detected by comparing the distribution index itself, which is an index indicating the width of the distribution of the feature amount, with the threshold value at predetermined time intervals. Therefore, unlike Patent Document 1, even if the movable device gradually breaks down, it is possible to detect the abnormality of the movable device. This makes it possible to stably detect an abnormality in the movable device.

The information processing apparatus according to the above aspect may further include a threshold value calculation unit that calculates the threshold value from an output value obtained by the obtaining unit in a second predetermined period that is a fixed period. According to the configuration, since the information processing apparatus automatically calculates the threshold, the work efficiency of the user can be improved as compared with a case where the user inputs the threshold.

In the information processing apparatus according to the one aspect, the distribution index may be a difference between a maximum value and a minimum value of the feature amount within the first predetermined period. According to the configuration, it is possible to reduce the load required for the calculation as compared with the case where the abnormality of the movable device is detected using the variance of the feature amount.

In the information processing apparatus according to the one aspect, the distribution index may be a variance of the feature amount or a skewness of a normal distribution within the first predetermined period. According to the configuration, the abnormality of the movable device can be stably detected.

In the information processing apparatus according to the one aspect, the output value may be a torque value of the motor, and the feature amount may be an average value of the torque values.

In the information processing apparatus according to the one aspect, the threshold value calculation unit calculates an average value and a standard deviation of the output values during the second predetermined period, and calculates the threshold value from a product of the average value and the standard deviation. May be.

In the information processing apparatus according to the one aspect, the abnormality detection unit may execute a process of stopping the rotation of the motor when detecting an abnormality of the movable device. Thereby, it is possible to prevent the movable device from being destroyed.

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

DESCRIPTION OF SYMBOLS 1 Movable system 10 Movable device 11 Base 12 Ball screw 13 Movable part 20 Coupling 30 Motor 40 Driver 50 PLC (information processing device)
Reference Signs List 51 acquisition unit 52 threshold value calculation unit 53 storage unit 54 feature value calculation unit 55 distribution index calculation unit 56 abnormality detection unit T1 threshold value

Claims

An information processing device that detects an abnormality of a movable device that performs a predetermined operation by rotation of a motor,
An acquisition unit that acquires an output value accompanying the rotation of the motor over time,
From the output value obtained by the obtaining unit, a feature amount calculating unit that calculates a feature amount for each of the predetermined operations,
A distribution index calculation unit that calculates a distribution index that is an index indicating a width of the distribution of the feature amount in a first predetermined period;
An information processing apparatus comprising: an abnormality detection unit that detects an abnormality of the movable device by comparing the distribution index with a threshold.
The information processing apparatus according to claim 1, further comprising: a threshold calculation unit configured to calculate the threshold from an output value acquired by the acquisition unit in a second predetermined period that is a fixed period.
The information processing apparatus according to claim 1 or 2, wherein the distribution index is a difference between a maximum value and a minimum value of the feature amount within the first predetermined period.
The information processing apparatus according to claim 1 or 2, wherein the distribution index is a variance of the feature amount or a skewness of a normal distribution within the first predetermined period.
The output value is a torque value of the motor,
The information processing apparatus according to any one of claims 1 to 4, wherein the characteristic amount is an average value of the torque values.
The information processing apparatus according to claim 2, wherein the threshold value calculation unit calculates an average value and a standard deviation of the output values in the second predetermined period, and calculates the threshold value from a product of the average value and the standard deviation. .
The information processing apparatus according to any one of claims 1 to 6, wherein the abnormality detection unit executes a process of stopping rotation of the motor when detecting an abnormality of the movable device.
An information processing method of an information processing device that detects an abnormality of a movable device that performs a predetermined operation by rotation of a motor,
An acquisition step of acquiring an output value accompanying the rotation of the motor over time,
A feature value calculating step of calculating a feature value for each of the predetermined operations from the output value obtained in the obtaining step;
A distribution index calculating step of calculating a distribution index that is an index indicating a width of the distribution of the feature amount in a first predetermined period;
An abnormality detection step of detecting an abnormality of the movable device by comparing the distribution index with a threshold.