Information processing method, information processing device, and program

Abstract

To obtain an information processing method that can appropriately present the user with the cause of an abnormal operation of a servo motor. [Solution] An information processing method for determining whether the operation of a servo motor controlling a controlled device is normal or abnormal, wherein the information processing device acquires a command signal to drive the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal, calculates the degree of abnormality of the servo motor's operation based on the command signal and the measurement data, determines whether the servo motor's operation is normal or abnormal based on the degree of abnormality, and displays the measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device.

Description

Technical Field

[0006] , , ,

[0005] , ,

[0001] The present disclosure relates to an information processing method, an information processing apparatus, and a program.

Background Art

[0002] An abnormal cause estimation method according to the background art is disclosed in, for example, Patent Document 1. In this abnormal cause estimation method, a first abnormality and a second abnormality are assumed as possible abnormalities, and an abnormality detection unit determines that the first abnormality or the second abnormality has occurred according to the detected value of the DC link voltage and the detected value of the input current to the DC link, and displays the determination result.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the abnormal cause estimation method according to the background art, since a first abnormality and a second abnormality are assumed as possible abnormalities, other abnormal causes cannot be appropriately presented to the user.

[0005] An object of the present disclosure is to obtain an information processing method, an information processing apparatus, and a program capable of appropriately presenting an abnormal cause of the operation of a servo motor to a user.

Means for Solving the Problems

[0006] An information processing method according to one aspect of the present disclosure is an information processing method for determining whether the operation of a servo motor controlling a controlled device is normal or abnormal, wherein the information processing device acquires a command signal for driving the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal, calculates the degree of abnormality of the servo motor's operation based on the command signal and the measurement data, determines whether the operation of the servo motor is normal or abnormal based on the degree of abnormality, and displays the measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device. [Effects of the Invention]

[0007] According to this disclosure, it becomes possible to appropriately present the cause of abnormal operation of the servo motor to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows a simplified configuration of a state determination device according to the embodiment of this disclosure. [Figure 2] This flowchart shows the processes performed by the information processing unit. [Figure 3] This figure shows an example of a screen displayed on an display device. [Figure 4] This diagram shows an example of setting the operating period. [Figure 5] This flowchart shows the processes performed by the information processing unit. [Figure 6] This figure shows an example of a screen displayed on an display device. [Figure 7] This flowchart shows the processes performed by the information processing unit. [Figure 8] This figure shows an example of a screen displayed on an display device. [Figure 9] This figure shows an example of a screen displayed on an display device. [Modes for carrying out the invention]

[0009] (Knowledge that forms the basis of this disclosure) In the method for estimating the cause of an anomaly related to the background technology, multiple possible causes of an anomaly are assumed in advance, and one cause of an anomaly is identified from among the multiple causes according to the measured values ​​of current or voltage during operation. For example, in the method for estimating the cause of an anomaly disclosed in Patent Document 1, a first anomaly and a second anomaly are assumed as possible anomalies, and the anomaly detection unit determines that a first anomaly or a second anomaly has occurred according to the detected value of the DC link voltage and the detected value of the input current to the DC link, and displays the determination result. Specifically, the anomaly detection unit determines that a first anomaly has occurred if the detected value of the DC link voltage is less than the first voltage threshold or greater than the second voltage threshold, and the detected value of the input current to the DC link is less than the current threshold, and determines that a second anomaly has occurred if the detected value of the input current is above the current threshold.

[0010] However, in the method for estimating the cause of anomalies related to the background technology, multiple possible causes of anomalies are assumed in advance, so it is not possible to present users with anomaly causes other than those assumed in advance.

[0011] To solve these problems, the inventors have come up with the present disclosure based on the discovery that the cause of the servo motor malfunction can be appropriately presented to the user by calculating the degree of abnormality of the servo motor's operation based on the command signal that drives the servo motor and measurement data measured with respect to the servo motor or the controlled device during operation, and by displaying the measurement data and degree of abnormality related to the operation determined to be abnormal on a display device.

[0012] Next, we will describe each aspect of this disclosure.

[0013] The information processing method according to the first aspect of the present disclosure is an information processing method for determining whether the operation of a servo motor that controls a controlled device is normal or abnormal. The information processing device acquires a command signal for driving the servo motor and measurement data measured regarding the servo motor or the controlled device when the servo motor operates based on the command signal, calculates the degree of abnormality of the operation of the servo motor based on the command signal and the measurement data, determines whether the operation of the servo motor is normal or abnormal based on the degree of abnormality, and causes the display device to display the measurement data and the degree of abnormality regarding the operation determined to be abnormal.

[0014] According to the first aspect, by causing the display device to display the measurement data and the degree of abnormality regarding the operation determined to be abnormal, the cause of the abnormality in the operation of the servo motor can be appropriately presented to the user.

[0015] The information processing method according to the second aspect of the present disclosure is, in the first aspect, the operation period of the servo motor has a plurality of operation periods including an acceleration period, a deceleration period, and a constant speed period. In the determination, whether the operation of the servo motor is normal or abnormal is determined based on whether the degree of abnormality is less than or equal to a predetermined threshold value or exceeds the threshold value. In the display, it is preferable that the number of times the degree of abnormality exceeds the threshold value for each operation period of the plurality of operation periods is further displayed.

[0016] According to the second aspect, by further displaying the number of times the degree of abnormality exceeds the threshold value for each operation period of the plurality of operation periods, a more detailed cause of the abnormality can be presented to the user.

[0017] The information processing method according to the third aspect of the present disclosure is, in the first or second aspect, the operation period of the servo motor has a plurality of operation periods including an acceleration period, a deceleration period, and a constant speed period. In the determination, whether the operation of the servo motor is normal or abnormal is determined according to whether the degree of abnormality is less than or equal to a predetermined threshold or exceeds the threshold. In the display, it is preferable that a data portion corresponding to the operation period in which the number of times the degree of abnormality in the measurement data exceeds the threshold is the largest is highlighted.

[0018] According to the third aspect, by highlighting the data portion corresponding to the operation period in which the number of times the degree of abnormality in the measurement data exceeds the threshold is the largest, the visibility of the user with respect to the important data portion can be improved.

[0019] The information processing method according to the fourth aspect of the present disclosure is, in any one of the first to third aspects, the operation period of the servo motor has a plurality of operation periods including an acceleration period, a deceleration period, and a constant speed period. In the display, it is preferable that a data portion corresponding to the operation period in which the degree of abnormality in the measurement data is the highest is highlighted.

[0020] According to the fourth aspect, by highlighting the data portion corresponding to the operation period in which the degree of abnormality in the measurement data is the highest, the visibility of the user with respect to the important data portion can be improved.

[0021] The information processing method according to the fifth aspect of the present disclosure is, in any one of the first to fourth aspects, in the calculation of the degree of abnormality, it is preferable to calculate the degree of abnormality using a machine-learned estimation model that estimates and outputs the degree of abnormality based on the input command signal and measurement data.

[0022] According to the fifth aspect, by using a machine-learned estimation model, the degree of abnormality can be calculated with high accuracy.

[0023] An information processing device according to a sixth aspect of the present disclosure is an information processing device for determining whether the operation of a servo motor that controls a controlled device is normal or abnormal, comprising: an acquisition unit that acquires a command signal for driving the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal; a calculation unit that calculates the degree of abnormality of the operation of the servo motor based on the command signal and the measurement data; a determination unit that determines whether the operation of the servo motor is normal or abnormal based on the degree of abnormality; and a display control unit that causes the measurement data and the degree of abnormality related to the operation determined to be abnormal to be displayed on a display device.

[0024] According to the sixth embodiment, the cause of the abnormal operation of the servo motor can be appropriately presented to the user by displaying measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device.

[0025] A program according to the seventh aspect of this disclosure is a program for causing an information processing device that determines whether the operation of a servo motor controlling a controlled device is normal or abnormal to function as follows: an acquisition means for acquiring a command signal to drive the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal; a calculation means for calculating the degree of abnormality of the servo motor's operation based on the command signal and the measurement data; a determination means for determining whether the servo motor's operation is normal or abnormal based on the degree of abnormality; and a display control means for displaying the measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device.

[0026] According to the seventh embodiment, the cause of the abnormal operation of the servo motor can be appropriately presented to the user by displaying measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device.

[0027] This disclosure can also be implemented as a program that causes a computer to execute each characteristic configuration included in such a method or apparatus, or as a system that operates using such a program. It goes without saying that such a computer program can be distributed via a computer-readable, non-temporary recording medium such as a CD-ROM, or via a communication network such as the Internet.

[0028] (Embodiments of the present disclosure) Embodiments of this disclosure will be described in detail below with reference to the drawings. Elements denoted by the same reference numeral in different drawings refer to the same or corresponding elements. Furthermore, the components, their arrangement, connection configurations, and operating sequences shown in the following embodiments are examples and are not intended to limit this disclosure. This disclosure is limited only by the claims. Therefore, among the components in the following embodiments, those not described in the independent claims representing the highest-level concepts of this disclosure are described as constituting a more preferable configuration, even though they are not necessarily required to achieve the object of this disclosure.

[0029] Figure 1 is a simplified diagram showing the configuration of a state determination device 20 according to an embodiment of this disclosure. The state determination device 20 determines whether the operation of the servo motor 13 that controls the controlled device 14 is normal or abnormal using a preset threshold H. The controlled device 14 is, for example, a production device used to manufacture equipment. The production device includes mounting devices, processing devices, machining devices, or transport devices for mounting, processing, working, or transporting equipment. The production device is installed, for example, on a factory production line. The servo motor 13 may be a rotary motor or a linear motor. The state determination device 20 may be a dedicated terminal, a general-purpose PC, or a server device. Furthermore, the functions of the state determination device 20 may be implemented in the motion controller 11.

[0030] An abnormality in the servo motor 13 includes not only an abnormality in the servo motor 13 itself, but also an abnormality in the controlled device 14.

[0031] The motion controller 11 outputs a command signal D1. The command signal D1 includes a position command signal, a speed command signal, or a torque command signal, etc., for specifying the movement position, movement speed, or generated torque of the servo motor 13. The servo amplifier 12 drives the servo motor 13 based on the command signal D1 input from the motion controller 11. The command signal D1 is input to the state determination device 20. The state determination device 20 is also input with respect to measurement data D3. The measurement data D3 is data measured with respect to the servo motor 13 or the controlled device 14 when the servo motor 13 operates based on the command signal D1. The measurement data D3 includes, for example, position data measured by a position sensor, torque data measured by a torque sensor, temperature data measured by a temperature sensor, or current data measured by a current sensor.

[0032] The state determination device 20 includes an information processing unit 21, a communication unit 22, an input device 23, a display device 24, and a storage unit 25.

[0033] The information processing unit 21 is configured using a processor such as a CPU. The information processing unit 21 has a data acquisition unit 31, a calculation unit 32, a display control unit 33, an information acquisition unit 34, a setting unit 35, a determination unit 36, a period setting unit 37, and a learning unit 38, as functions realized by the processor executing a program read from a computer-readable non-volatile recording medium such as a ROM. In other words, the above program is a program that causes the information processing unit 21, as an information processing device mounted on the state determination device 20, to function as a data acquisition unit 31 (data acquisition means), a calculation unit 32 (calculation means), a display control unit 33 (display control means), an information acquisition unit 34 (information acquisition means), a setting unit 35 (setting means), a determination unit 36 ​​(determination means), a period setting unit 37 (period setting means), and a learning unit 38 (learning means). Details of the processing content executed by each processing unit will be described later.

[0034] The communication unit 22 is configured with a communication module that supports any communication method, such as a dedicated network or a public network.

[0035] The input device 23 is configured to include a mouse, keyboard, or touch panel that can be operated by the user.

[0036] The display device 24 is configured to include a liquid crystal display or an organic EL display, etc., that is visible to the user operating the input device 23.

[0037] The storage unit 25 is configured to include an HDD, SSD, or semiconductor memory. The storage unit 25 holds an estimation model 41, a command signal 42, and measurement data 43. The estimation model 41 is a machine learning-prepared estimation model that uses the command signal D1 and measurement data D3 as explanatory variables and the degree of abnormality of the servo motor 13's operation as the objective variable. The estimation model 41 is machine learning-prepared, for example, by unsupervised learning using a large amount of normal data by the learning unit 38. Based on the input command signal D1 and measurement data D3, the estimation model 41 estimates and outputs the degree of abnormality N using a predetermined algorithm. For example, based on the speed command signal included in the command signal D1 and the torque data included in the measurement data D3, the estimation model 41 estimates and outputs the degree of abnormality N using an algorithm such as Mahalanobis distance, k-NN, decision tree, SVM, or Naive Bayes. The anomaly score N is an index that represents the degree of deviation from normal data. The greater the deviation from normal data, the higher the value of the anomaly score N, and the smaller the deviation from normal data, the lower the value of the anomaly score N.

[0038] <Setting the threshold> Figure 2 is a flowchart showing the process that the information processing unit 21 performs regarding the setting of the threshold H.

[0039] First, in step S11, the data acquisition unit 31 acquires a command signal D1 for driving the servo motor 13 and measurement data D3 measured with respect to the servo motor 13 or the controlled device 14 when the servo motor 13 operates based on the command signal D1. The command signal D1 and measurement data D3 to be acquired may be a command signal D1 and measurement data D3 corresponding to a specific single operation, or they may be statistical values ​​(e.g., average values) of command signals D1 and measurement data D3 corresponding to multiple past operations. Command signals D1 and measurement data D3 corresponding to multiple past operations are compiled into a database as command signals 42 and measurement data 43 and stored in the storage unit 25.

[0040] Next, in step S12, the calculation unit 32 inputs the command signal D1 and measurement data D3 acquired in step S11 to the estimation model 41, and calculates the degree of abnormality N of the servo motor 13's operation as an output from the estimation model 41. Note that the method used by the calculation unit 32 to calculate the degree of abnormality N is not limited to the method using the estimation model 41, but may also be a rule-based calculation method or the like.

[0041] Next, in step S13, the display control unit 33 generates image data D5 including the measurement data D3 acquired in step S11 and the anomaly degree N calculated in step S12, and inputs the image data D5 to the display device 24, thereby causing the display device 24 to display the measurement data D3 and the anomaly degree N.

[0042] Figure 3 shows an example of a screen displayed on the display device 24 regarding the setting of the threshold H. The display device 24 displays side by side a screen showing the time-series measurement data X indicated by the measurement data D3, and a screen showing the time-series anomaly degree N corresponding to the measurement data X. For example, the horizontal axis of the screen showing the measurement data X is time, and the vertical axis is the measured value of the torque data.

[0043] The operating period of the servo motor 13 is divided into multiple operating periods P, including an acceleration period P1, a deceleration period P2, and a constant speed period P3. The constant speed period P3 is further divided into a transient period P3a, which includes the beginning of the constant speed period P3, and a steady-state period P3b, which includes the end of the constant speed period P3. The transient period P3a is the period in which the speed command data value is zero, but the measured value of the torque data or speed data is greater than a predetermined value due to inertia. The steady-state period P3b is the period in which the measured value of the torque data or speed data is less than or equal to a predetermined value. The operating period is set by the period setting unit 37. The measured data X includes measured data X1 belonging to the acceleration period P1, measured data X2 belonging to the deceleration period P2, measured data X3a belonging to the transient period P3a, and measured data X3b belonging to the steady-state period P3b.

[0044] Figure 4 shows an example of setting the operating period P by the period setting unit 37. First, the period setting unit 37 acquires the position command signal included in the command signal D1, as shown in (A). Next, the period setting unit 37 calculates the velocity command data by differentiating the position command signal, as shown in (B). Next, the period setting unit 37 calculates the acceleration command data by differentiating the velocity command data, as shown in (C). The period setting unit 37 sets the period during which the absolute value of the acceleration is greater than or equal to a certain value and has a positive sign as the acceleration period P1. The period setting unit 37 also sets the period during which the absolute value of the acceleration is greater than or equal to a certain value and has a negative sign as the deceleration period P2. The period setting unit 37 also sets the period during which the absolute value of the acceleration is less than a certain value as the constant speed period P3. The period setting unit 37 also sets the period before a predetermined time has elapsed from the start of the constant speed period P3 as the transient period P3a, and the period after a predetermined time has elapsed from the start of the constant speed period P3 as the steady-state period P3b. The period setting unit 37 may set the period during the constant speed period P3 in which the measured value of torque data is equal to or greater than a predetermined value as the transient period P3a, and the period during the constant speed period P3 in which the measured value of torque data is less than a predetermined value as the steady-state period P3b. Alternatively, the period setting unit 37 may set the period during the constant speed period P3 in which the measured value of speed data is equal to or greater than a predetermined value as the transient period P3a, and the period during the constant speed period P3 in which the measured value of speed data is less than a predetermined value as the steady-state period P3b.

[0045] Next, in step S14, the information acquisition unit 34 acquires setting information D4 of the allowable range Z, which is variably set by user operation based on the measurement data X displayed on the display device 24, from the input device 23.

[0046] The user can set an allowable range Z1 defined by an upper limit Y1U and a lower limit Y1L for the acceleration period P1 by, for example, dragging the measurement data X1 vertically using a mouse. The user can also set an allowable range Z2 defined by an upper limit Y2U and a lower limit Y2L for the deceleration period P2 by, for example, dragging the measurement data X2 vertically using a mouse. The user can also set an allowable range Z3a defined by an upper limit Y3aU and a lower limit Y3aL for the transient period P3a by, for example, dragging the measurement data X3a vertically using a mouse. The user can also set an allowable range Z3b defined by an upper limit Y3bU and a lower limit Y3bL for the steady-state period P3b by, for example, dragging the measurement data X3b vertically using a mouse. The information acquisition unit 34 acquires setting information D4 for the allowable ranges Z1, Z2, Z3a, and Z3b from the input device 23.

[0047] Furthermore, the tolerance range Z can be set not only by dragging with a mouse, but also by moving a slider bar or by entering numerical values. Also, in the example in Figure 3, the same width of tolerance range Z is set for both the excess direction (upward) and the deficiency direction (downward) with respect to the measurement data X, but the width of the tolerance range Z can also be set individually for the excess direction and the deficiency direction. For example, for the acceleration period P1, the upper limit Y1U is set by dragging the measurement data X1 upward, and the lower limit Y1L is set separately from the upper limit Y1U by dragging the measurement data X1 downward, and the tolerance range Z1 defined by the upper limit Y1U and the lower limit Y1L is set.

[0048] Next, in step S15, the setting unit 35 sets a threshold H for each operating period P based on the setting information D4 acquired in step S14. The setting unit 35 sets a threshold H according to the set width of the allowable range Z. In the example in Figure 3, the setting unit 35 sets the largest threshold H1 for the acceleration period P1, which has the largest set width of the allowable range Z, and sets the next largest threshold H2 for the deceleration period P2, which has the next largest set width of the allowable range Z. The setting unit 35 also sets the smallest threshold H3b for the steady-state period P3b, which has the smallest set width of the allowable range Z, and sets the next smallest threshold H3a for the transient period P3a, which has the next smallest set width of the allowable range Z.

[0049] During actual operation of the servo motor 13, the calculation unit 32 calculates the degree of abnormality N using the estimation model 41 based on the command signal D1 and measurement data D3. The determination unit 36 ​​determines that the operation of the servo motor 13 is abnormal if the calculated degree of abnormality N exceeds the threshold H, and determines that the operation of the servo motor 13 is normal if the calculated degree of abnormality N is less than or equal to the threshold H.

[0050] Furthermore, if the tolerance range Z is set individually for the excess and deficiency directions relative to the measurement data X, the setting unit 35 sets the threshold H individually for the excess and deficiency directions.

[0051] Next, in step S16, the display control unit 33 further displays the threshold H set in step S15 on the display device 24, corresponding to the degree of abnormality acquired in step S11. In the example in Figure 3, thresholds H1, H2, H3a, and H3b are displayed, corresponding to the degree of abnormality N.

[0052] The display control unit 33 may increase or decrease the threshold H in conjunction with the setting of the tolerance range Z by user operation and display it on the display device 24. For example, if the user expands the setting width of the tolerance range Z1 by dragging the mouse, the display control unit 33 increases the threshold H1 in real time in accordance with the expansion of the setting width of the tolerance range Z1 and displays the changed threshold H1 on the display device 24. Also, for example, if the user reduces the setting width of the tolerance range Z2 by dragging the mouse, the display control unit 33 decreases the threshold H2 in real time in accordance with the reduction of the setting width of the tolerance range Z2 and displays the changed threshold H2 on the display device 24.

[0053] Alternatively, the system may be configured to allow the user to directly adjust the threshold H displayed on the display device 24 by dragging the mouse, etc. For example, if the user moves threshold H1 upward by dragging the mouse, the information acquisition unit 34 acquires adjustment information, including the direction and amount of movement of threshold H1, from the input device 23, and the setting unit 35 increases the set value of threshold H1 according to the amount of movement based on the adjustment information. Also, for example, if the user moves threshold H2 downward by dragging the mouse, the information acquisition unit 34 acquires adjustment information, including the direction and amount of movement of threshold H2, from the input device 23, and the setting unit 35 decreases the set value of threshold H2 according to the amount of movement based on the adjustment information.

[0054] At this time, the display control unit 33 may expand or contract the tolerance range Z in conjunction with the increase or decrease of the threshold H by user operation and display it on the display device 24. For example, if the user moves the threshold H1 upward by dragging the mouse, the display control unit 33 expands the setting width of the tolerance range Z1 in real time according to the increased threshold H1 and displays the expanded tolerance range Z1 on the display device 24. Also, for example, if the user moves the threshold H2 downward by dragging the mouse, the display control unit 33 contracts the setting width of the tolerance range Z2 in real time according to the decreased threshold H2 and displays the contracted tolerance range Z2 on the display device 24.

[0055] According to this embodiment, the display control unit 33 displays the measurement data D3 and the degree of abnormality N on the display device 24, and the information acquisition unit 34 acquires setting information D4 of the allowable range Z, which is set variably by user operation based on the displayed measurement data D3, from the input device 23. This makes it possible for the determination unit 36 ​​to determine whether the operation of the servo motor 13 is normal or abnormal to set a threshold H variably by user operation.

[0056] Furthermore, according to this embodiment, by setting threshold values ​​H1 to H3 individually for each of the multiple operating periods P1 to P3, it becomes possible to detect anomalies in detail.

[0057] Furthermore, according to this embodiment, by setting threshold values ​​H3a and H3b individually for the transient period P3a and the steady-state period P3b, even more precise anomaly detection becomes possible.

[0058] Furthermore, according to this embodiment, by individually setting threshold values ​​H for the excess and deficiency directions of the measurement data D3, it becomes possible to detect anomalies in detail.

[0059] Furthermore, according to this embodiment, user convenience can be improved by increasing or decreasing the threshold H in conjunction with the user's setting of the tolerance range Z and displaying it on the display device 24.

[0060] Furthermore, according to this embodiment, not only can the tolerance range Z be set, but the threshold H can also be directly adjusted by user operation, thereby improving user convenience.

[0061] Furthermore, according to this embodiment, user convenience can be further improved by increasing or decreasing the allowable range Z in conjunction with the user's adjustment of the threshold H and displaying it on the display device 24.

[0062] Furthermore, according to this embodiment, the calculation unit 32 can calculate the anomaly score N with high accuracy by using the machine learning-trained estimation model 41.

[0063] Figure 5 is a flowchart showing the processes that the information processing unit 21 performs with respect to variations in the setting of the threshold H.

[0064] First, in step S21, the data acquisition unit 31 acquires, similar to step S11, a command signal D1 for driving the servo motor 13 and measurement data D3 measured with respect to the servo motor 13 or the controlled device 14 when the servo motor 13 operates based on the command signal D1.

[0065] Next, in step S22, the calculation unit 32 inputs the command signal D1 and measurement data D3 acquired in step S21 to the estimation model 41, similar to step S12, and calculates the degree of abnormality N of the servo motor 13's operation as an output from the estimation model 41.

[0066] Next, in step S23, the setting unit 35 sets a reference threshold H0 based on the command signal S1 and measurement data D3 acquired in step S21. For example, the setting unit 35 calculates multiple time-series anomaly degrees N based on the command signal D1 and measurement data D3, and sets the value obtained by adding k times the standard deviation σ of the anomaly degrees N to the maximum value of the anomaly degrees N as the reference threshold H0.

[0067] Next, in step S24, the display control unit 33 inputs the generated image data D5 to the display device 24, thereby displaying the measurement data D3, the degree of abnormality N, and the reference threshold H0 corresponding to the degree of abnormality N on the display device 24.

[0068] Figure 6 shows an example of a screen displayed on the display device 24. The display device 24 displays side by side a screen showing the time-series measurement data X indicated by measurement data D3, and a screen showing the time-series anomaly degree N corresponding to the measurement data X. In addition, the reference threshold H0 is displayed corresponding to the anomaly degree N.

[0069] Next, in step S25, the information acquisition unit 34 acquires setting information D4 of a threshold H, which is set variably by user operation based on the reference threshold H0 displayed on the display device 24, from the input device 23.

[0070] For example, if a user moves the reference threshold H0 upward during the acceleration period P1 by dragging the mouse, the information acquisition unit 34 acquires setting information D4 from the input device 23, which includes the direction and amount of movement of the reference threshold H0. Based on the setting information D4, the setting unit 35 sets a threshold H1 that is greater than the reference threshold H0 according to the amount of movement. Also, for example, if a user moves the reference threshold H0 downward during the transient period P3a by dragging the mouse, the information acquisition unit 34 acquires setting information D4 from the input device 23, which includes the direction and amount of movement of the reference threshold H0. Based on the setting information D4, the setting unit 35 sets a threshold H3a that is smaller than the reference threshold H0 according to the amount of movement.

[0071] Next, in step S26, the setting unit 35 sets the allowable range Z of the measurement data X based on the threshold H set for each operating period P.

[0072] Next, in step S27, the display control unit 33 displays the tolerance range Z set in step S26 on the display device 27, corresponding to the measurement data X.

[0073] At this time, the display control unit 33 may expand or contract the tolerance range Z in conjunction with the movement of the reference threshold H0 by user operation and display it on the display device 24. For example, if the user moves the reference threshold H0 in the acceleration period P1 upward by dragging the mouse, the display control unit 33 expands the setting width of the tolerance range Z1 in real time according to the increased threshold H1 and displays the expanded tolerance range Z1 on the display device 24. Also, for example, if the user moves the reference threshold H0 in the transient period P3a downward by dragging the mouse, the display control unit 33 contracts the setting width of the tolerance range Z3a in real time according to the decreased threshold H3a and displays the contracted tolerance range Z3a on the display device 24.

[0074] According to this modified example, the display control unit 33 displays the measurement data X, the degree of abnormality N, and the reference threshold H0 on the display device 24, and the information acquisition unit 34 acquires setting information D4 of the threshold H, which is set variably by user operation based on the reference threshold H0 displayed on the display device 24, from the input device 23. This makes it possible to set the threshold H for determining whether the operation of the servo motor 13 is normal or abnormal variably by user operation.

[0075] Furthermore, according to this modified example, user convenience can be improved by increasing or decreasing the allowable range Z in conjunction with the user's setting of the threshold H and displaying it on the display device 24.

[0076] <Anomaly detection during actual operation> Figure 7 is a flowchart showing the processes executed by the information processing unit 21 in relation to abnormality detection during the actual operation of the servo motor 13.

[0077] First, in step S31, the determination unit 36 ​​determines whether or not the operation of the controlled device 14 has been forcibly stopped due to a trouble or the like.

[0078] If the controlled device 14 is not forcibly stopped (step S31: NO), the process in step S31 is repeatedly executed.

[0079] If the controlled device 14 is forcibly stopped (step S31: YES), then in step S32, the data acquisition unit 31 acquires a command signal D1 for driving the servo motor 13 and measurement data D3 measured with respect to the servo motor 13 or the controlled device 14 when the servo motor 13 operates based on the command signal D1. The command signal D1 and measurement data D3 to be acquired may be the command signal D1 and measurement data D3 corresponding to a single operation that was forcibly stopped, or they may be statistical values ​​(e.g., average values) of the command signal D1 and measurement data D3 corresponding to multiple operations immediately before the forcible stop. The command signal D1 and measurement data D3 corresponding to multiple operations are compiled into a database as command signal 42 and measurement data 43 and stored in the storage unit 25. Note that the processing in step S32 may be executed not only when the controlled device 14 is forcibly stopped, but also when the abnormality analysis mode is started by user operation or the like.

[0080] Next, in step S33, the calculation unit 32 inputs the command signal D1 and measurement data D3 acquired in step S32 to the estimation model 41, and calculates the degree of abnormality N of the servo motor 13's operation as an output from the estimation model 41. Note that the method used by the calculation unit 32 to calculate the degree of abnormality N is not limited to the method using the estimation model 41, but may also be a rule-based calculation method or the like.

[0081] Next, in step S34, the determination unit 36 ​​determines whether the abnormality level N calculated in step S32 exceeds a preset threshold H. If the abnormality level N exceeds the threshold H in any of the multiple operating periods P, the determination unit 36 ​​determines that the operation of the servo motor 13 is abnormal. On the other hand, if the abnormality level N is less than or equal to the threshold H in all of the multiple operating periods P, the determination unit 36 ​​determines that the operation of the servo motor 13 is normal.

[0082] If the abnormality level N is less than or equal to the threshold H (step S34: NO), the information processing unit 21 terminates processing. In this case, the display control unit 33 may display a message on the display device 24 prompting maintenance of the controlled device 14.

[0083] If the abnormality level N exceeds the threshold H (step S34: YES), then in step S35, the display control unit 33 generates image data D5 which includes the measurement data D3 acquired in step S32, the abnormality level N calculated in step S33, and the cause of the abnormality in the operation of the servo motor 13. The cause of the abnormality includes the number of abnormalities or the maximum abnormality level. The display control unit 33 inputs the image data D5 to the display device 24, thereby displaying this information regarding the operation determined to be abnormal on the display device 24.

[0084] Figure 8 shows an example of the screen displayed on the display device 24 regarding abnormality detection during the actual operation of the servo motor 13. The display device 24 displays, side by side, a screen showing the time-series measurement data X indicated by measurement data D3, a screen showing the time-series abnormality degree N corresponding to the measurement data X, and a screen showing the number of abnormalities in each operating period P as the cause of the abnormality. The number of abnormalities indicates the total number of times the abnormality degree N exceeded the threshold H for each operating period P. In the example shown in Figure 8, the number of abnormalities is 1 for the acceleration period P1, 1 for the deceleration period P2, 3 for the transient period P3a, and 2 for the steady-state period P3b. Therefore, the number of abnormalities is highest for the transient period P3a.

[0085] The display control unit 33 may highlight the data portion of each time-series data of measurement data X and anomaly degree N that corresponds to the operating period P (transient period P3a in this example) with the highest number of anomalies by coloring. The display control unit 33 may also highlight the screen portion of the screen showing the number of anomalies that corresponds to the operating period P (transient period P3a in this example) with the highest number of anomalies by coloring. Furthermore, instead of coloring, the display control unit 33 may highlight by enlarging or surrounding with a frame. This allows the user to easily recognize that the cause of the anomaly in the operation of the servo motor 13 is the transient period P3a, and can be used as a clue for verifying the actual operation of the servo motor 13 or the controlled device 14, resetting the threshold H, or for additional learning by the learning unit 38.

[0086] Figure 9 shows another example of a screen displayed on the display device 24 regarding anomaly detection during actual operation of the servo motor 13. The display device 24 displays, side by side, a screen showing the time-series measurement data X indicated by measurement data D3, a screen showing the time-series anomaly degree N corresponding to the measurement data X, and a screen showing the maximum anomaly degree for each operating period P as the cause of the anomaly. The maximum anomaly degree indicates the maximum value of the anomaly degree N for each operating period P. In the example shown in Figure 9, the maximum anomaly degree is 0.2 for the acceleration period P1, 0.3 for the deceleration period P2, 0.3 for the transient period P3a, and 0.5 for the steady-state period P3b. Therefore, the maximum anomaly degree is highest during the steady-state period P3b.

[0087] The display control unit 33 may highlight, by color, the portion of the time-series data of measurement data X and anomaly degree N that corresponds to the operating period P (steady-state period P3b in this example) with the highest maximum anomaly degree. The display control unit 33 may also highlight, by color, the portion of the screen showing the maximum anomaly degree that corresponds to the operating period P (steady-state period P3b in this example) with the highest maximum anomaly degree. Furthermore, instead of coloring, the display control unit 33 may highlight by enlarging or surrounding with a frame. This allows the user to easily recognize that the cause of the anomaly in the operation of the servo motor 13 is the steady-state period P3b, and can be used as a clue for verifying the actual operation of the servo motor 13 or the controlled device 14, resetting the threshold H, or for additional learning by the learning unit 38.

[0088] According to this embodiment, the measurement data X and the degree of abnormality N related to the operation determined to be abnormal are displayed on the display device 24, thereby appropriately presenting the user with the cause of the abnormal operation of the servo motor 13.

[0089] Furthermore, as shown in the example screen in Figure 8, the number of abnormalities for each operating period P is displayed on the display device 24, allowing for more detailed information about the cause of the abnormality to be presented to the user.

[0090] Furthermore, as shown in the example screen in Figure 9, the maximum abnormality level for each operating period P is displayed on the display device 24, allowing for more detailed information about the cause of the abnormality to be presented to the user. [Industrial applicability]

[0091] This disclosure is broadly applicable to servo motor anomaly detection systems. [Explanation of Symbols]

[0092] 13 Servo motors 14. Controlled Device 21 Information Processing Department 23 Input device 24 Display device 25 Memory section 31 Data Acquisition Unit 32 Calculation Section 33 Display Control Unit 34 Information Acquisition Department 35 Settings Section 36 Judgment section 41 Estimated Models

Claims

1. An information processing method for determining whether the operation of a servo motor that controls a controlled device is normal or abnormal, Information processing device, The command signal for driving the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal are acquired. Based on the command signal and the measurement data, the degree of abnormality in the operation of the servo motor is calculated. Based on the degree of abnormality, it is determined whether the operation of the servo motor is normal or abnormal. The measurement data and the degree of abnormality related to the operation determined to be abnormal are displayed on the display device. Information processing methods.

2. The operating period of the servo motor has a plurality of operating periods, including an acceleration period, a deceleration period, and a constant speed period. In the above determination, it is determined whether the operation of the servo motor is normal or abnormal based on whether the degree of abnormality is below a predetermined threshold or exceeds the threshold. In the above display, the number of times the degree of abnormality exceeds the threshold for each of the multiple operating periods is further displayed. The information processing method according to claim 1.

3. The operating period of the servo motor has a plurality of operating periods, including an acceleration period, a deceleration period, and a constant speed period. In the above determination, it is determined whether the operation of the servo motor is normal or abnormal based on whether the degree of abnormality is below a predetermined threshold or exceeds the threshold. In the above display, the portion of the measurement data corresponding to the operating period in which the number of times the abnormality level exceeds the threshold is highest is highlighted. The information processing method according to claim 1.

4. The operating period of the servo motor has a plurality of operating periods, including an acceleration period, a deceleration period, and a constant speed period. In the aforementioned display, the portion of the measurement data corresponding to the operating period with the highest degree of abnormality is highlighted. The information processing method according to claim 1.

5. In calculating the degree of abnormality, the degree of abnormality is calculated using a machine learning-prepared estimation model that estimates and outputs the degree of abnormality based on the input command signal and measurement data. The information processing method according to claim 1.

6. An information processing device that determines whether the operation of a servo motor controlling a controlled device is normal or abnormal, An acquisition unit that acquires a command signal for driving the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal. A calculation unit that calculates the degree of abnormality in the operation of the servo motor based on the command signal and the measurement data, A determination unit that determines whether the operation of the servo motor is normal or abnormal based on the degree of abnormality, A display control unit that displays the measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device, An information processing device equipped with the following features.

7. An information processing device that determines whether the operation of a servo motor controlling a controlled device is normal or abnormal. An acquisition means for acquiring a command signal for driving the servo motor and measurement data measured with respect to the servo motor or the controlled device when the servo motor operates based on the command signal, A calculation means for calculating the degree of abnormality in the operation of the servo motor based on the command signal and the measurement data, A determination means for determining whether the operation of the servo motor is normal or abnormal based on the degree of abnormality, A display control means for displaying the measurement data and the degree of abnormality related to the operation determined to be abnormal on a display device, A program designed to function as such.

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