Diagnostic method, diagnostic device, and diagnostic system

Abstract

To provide a diagnostic method capable of enhancing reliability of state diagnosis of an object.SOLUTION: A diagnostic method includes: a step of obtaining first measurement data based on physical quantity caused by an object repeating a predetermined operation pattern in a first period; a reference data generation step of generating reference data based on the first measurement data; a step of obtaining second measurement data based on physical quantity caused by the object repeating the predetermined operation pattern in a second period; and a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data. The reference data generation step includes steps of: extracting a plurality of first period unit data respectively corresponding to at least a part of the predetermined operation pattern from the first measurement data; and generating the reference data by calculating a representative value of the plurality of first period unit data processed synchronously.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a diagnostic method, a diagnostic device, and a diagnostic system. [Background technology]

[0002] Patent document 1 describes a failure prediction device that has a vibration detection means that detects the vibration patterns generated by each of the multiple moving parts that make up a production machine at the free end of the moving part, a storage means that stores the reference vibration pattern at the free end when the production machine is operating normally, and a failure prediction means that compares the vibration patterns detected by the vibration detection means at any time with the reference vibration patterns stored in the storage means to predict failures in the production machine. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 5-52712 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 does not describe a specific method for creating a reference vibration pattern, and if the accuracy of the reference vibration pattern is low, the reliability of failure prediction decreases. [Means for solving the problem]

[0005] One aspect of the diagnostic method according to the present invention is to a first measurement data acquisition step of acquiring first measurement data based on a time-series signal obtained by detecting a physical quantity generated by the object repeating a predetermined movement pattern during a first period using a physical quantity sensor; a reference data generating step of generating reference data based on the first measurement data; a second measurement data acquisition step of acquiring second measurement data based on a time-series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeating the predetermined movement pattern during a second period; a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data; Including, The reference data generating step includes: extracting a plurality of first period unit data from the first measurement data, each of which corresponds to at least a part of the predetermined movement pattern; a step of performing a synchronization process on the plurality of first period unit data and calculating a representative value of the plurality of first period unit data that have been synchronized to generate the reference data; Includes.

[0006] One aspect of the diagnostic device according to the present invention is a first measurement data acquisition circuit that acquires first measurement data based on a time-series signal obtained by a physical quantity sensor detecting a physical quantity generated by a target object repeating a predetermined movement pattern during a first period; a reference data generating circuit that generates reference data based on the first measurement data; a second measurement data acquisition circuit that acquires second measurement data based on a time-series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeating the predetermined movement pattern during a second period; and a diagnostic circuit that diagnoses a state of the object based on the reference data and the second measurement data; Including, The reference data generating circuit A plurality of first period unit data corresponding to at least a portion of the predetermined operation pattern are extracted from the first measurement data, the plurality of first period unit data are synchronized, and a representative value of the synchronized plurality of first period unit data is calculated to generate the reference data.

[0007] One aspect of the diagnostic system according to the present invention is One aspect of the diagnostic device; the physical quantity sensor attached to the object; Equipped with. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 3 is a flowchart showing the procedure of the diagnostic method according to the first embodiment. [Figure 2] FIG. 10 is a flowchart showing an example of a procedure of a reference data generating step. [Figure 3] FIG. 4 is a diagram showing a portion of first measurement data in the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining synchronization processing in the first embodiment. [Figure 5] FIG. 4 is a diagram for explaining synchronization processing in the first embodiment. [Figure 6] FIG. 4 is a diagram for explaining synchronization processing in the first embodiment. [Figure 7] FIG. 4 is a diagram showing the waveform of reference data in the first embodiment. [Figure 8] FIG. 10 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the reference data. [Figure 9] FIG. 10 is a flowchart showing an example of the procedure of a diagnostic process. [Figure 10] FIG. 1 is a diagram showing an example of a Lissajous figure. [Figure 11] FIG. 1 is a diagram showing an example of the configuration of a diagnostic device. [Figure 12] FIG. 10 is a diagram showing a portion of first measurement data in the second embodiment. [Figure 13] FIG. 10 is a diagram for explaining synchronization processing in the second embodiment. [Figure 14] FIG. 10 is a diagram for explaining synchronization processing in the second embodiment. [Figure 15] FIG. 10 is a diagram showing the waveform of reference data in the second embodiment. [Figure 16] FIG. 11 is a flowchart showing an example of the procedure of a diagnostic process in a diagnostic method according to a third embodiment. [Figure 17]FIG. 13 is a flowchart showing an example of a procedure of a reference data generating step in the fourth embodiment. [Figure 18] FIG. 13 is a diagram showing an example of the relationship between the first measurement data and a plurality of first period unit data corresponding to the i-th operation in the fourth embodiment. [Figure 19] FIG. 13 is a flowchart showing an example of the procedure of a diagnostic process in the fourth embodiment. [Figure 20] FIG. 11 is a flowchart showing another example of the procedure of the diagnostic process in the diagnostic method of the fourth embodiment. [Figure 21] FIG. 1 is a diagram showing an example of the configuration of a diagnostic system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0009] Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Note that the embodiments described below do not unduly limit the content of the present invention as defined in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention.

[0010] 1. Diagnostic method and diagnostic device 1-1. First embodiment 1-1-1. Diagnostic method Fig. 1 is a flowchart showing the steps of a diagnostic method according to a first embodiment. As shown in Fig. 1, the diagnostic method according to the first embodiment includes a first measurement data acquisition step S1, a reference data generation step S2, a second measurement data acquisition step S4, and a diagnostic step S5. The diagnostic method according to the first embodiment is executed by, for example, a diagnostic device 100. An example of the configuration of the diagnostic device 100 that executes the diagnostic method according to the first embodiment will be described later.

[0011] As shown in FIG. 1, first, in a first measurement data acquisition step S1, the diagnostic device 100 acquires first measurement data based on a time-series signal obtained by a physical quantity sensor detecting a physical quantity generated by an object repeating a predetermined movement pattern during a first period.

[0012] The first period may be a predetermined period during which the object operates normally, such as immediately after the object is installed.

[0013] The object is the object to be diagnosed, and its type is not particularly limited; for example, it may be various devices such as electric motors or motors having a rotating mechanism or a vibrating mechanism, or it may be an electrical circuit that generates a periodic signal.

[0014] The predetermined motion pattern repeated by the object during the first period may be a pattern in which the object performs one type of motion and then stops the motion, or a pattern in which the object stops the motion after performing each of a plurality of different types of motions. For example, if the object is a motor, the object may repeatedly perform a motion of rotating clockwise and stopping the rotation, or may repeatedly perform a motion of rotating clockwise, stopping the rotation, a motion of rotating counterclockwise, and stopping the rotation.

[0015] The type of physical quantity that is generated when an object repeats a predetermined motion pattern is not particularly limited, and the physical quantity may be, for example, acceleration, angular velocity, velocity, displacement, pressure, current, voltage, or the like.

[0016] For example, the physical quantity sensor may be an inertial sensor. The inertial sensor may be, for example, an acceleration sensor, a velocity sensor, an angular velocity sensor, or an IMU equipped with multiple types of sensors. IMU stands for Inertial Measurement Unit. The physical quantity sensor may be, for example, a sensor using a MEMS resonator or a sensor using a quartz resonator. MEMS stands for Micro Electro Mechanical Systems. The physical quantity sensor may have one or more detection axes.

[0017] The first measurement data may be time series data of a digital signal output from a physical quantity sensor, or time series data of a digital signal obtained by converting an analog signal output from the physical quantity sensor by an analog front end.

[0018] Next, in a reference data generating step S2, the diagnostic device 100 generates reference data based on the first measurement data acquired in step S1.

[0019] Next, the diagnostic device 100 waits until a set time has elapsed in step S3. After the set time has elapsed, the diagnostic device 100 acquires second measurement data in a second measurement data acquisition step S4 based on a time-series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeating a predetermined movement pattern during a second period.

[0020] In this embodiment, the second period is a period after the first period, such as a predetermined period of time several days, several months, several years, etc. The predetermined movement pattern repeated by the object in the second period is the same as the predetermined movement pattern repeated by the object in the first period.

[0021] The second measurement data may be time series data of a digital signal output from a physical quantity sensor, or time series data of a digital signal obtained by converting an analog signal output from the physical quantity sensor by an analog front end.

[0022] Next, in a diagnosis step S5, the diagnosis device 100 diagnoses the state of the object based on the reference data generated in step S2 and the second measurement data acquired in step S4.

[0023] The diagnostic device 100 may diagnose whether the object is in a normal state or an abnormal state in a second time period, assuming that the object is in a normal state in a first time period. Furthermore, the diagnostic device 100 may diagnose how much the object has changed over time from the first time period to the second time period.

[0024] Then, diagnostic device 100 repeats steps S3 to S5 until the diagnosis is completed (N in step S6). The set waiting time in step S3 may be a fixed value or a variable value that is set appropriately each time.

[0025] FIG. 2 is a flowchart showing an example of the procedure of the reference data generation step S2 in FIG. 1. As shown in FIG. 2, first, in step S21, the diagnostic device 100 extracts, from the first measurement data acquired in step S1 in FIG. 1, a plurality of first period unit data corresponding to at least a part of a predetermined movement pattern repeated by the object in a first period. For example, if the predetermined movement pattern is a pattern in which the object performs one type of movement and then stops the movement, each of the plurality of first period unit data may be data corresponding to that movement. Also, for example, if the predetermined movement pattern is a pattern in which the object stops the movement after performing each of a plurality of different types of movement, each of the plurality of first period unit data may be data corresponding to the plurality of movements.

[0026] Next, in step S22, the diagnostic device 100 displays the plurality of first period unit data extracted in step S21 on a display unit (not shown).

[0027] Finally, in step S23, the diagnostic device 100 synchronizes the plurality of first period unit data extracted in step S21 and generates reference data by calculating a representative value of the synchronized plurality of first period unit data. The synchronization process is a process of aligning the timing so that the difference between a predetermined data included in the plurality of first period unit data and each of the other data is minimized. The representative value is, for example, an average value or a median value.

[0028] Fig. 3 is a diagram showing a portion of the first measurement data acquired in the first measurement data acquisition step S1. The first measurement data shown in Fig. 3 is a portion of speed data based on a time-series signal output from a speed sensor, which is a physical quantity sensor, when a motor, which is an object, repeats a predetermined operation pattern consisting of a first operation, a stop, a second operation, and a stop during a first period. As shown in Fig. 3, in step S21, the diagnosis device 100 extracts, from the first measurement data, data when the motor performs the first operation, a stop, and a second operation, which are part of the predetermined operation pattern, for the nth time, as the nth first period unit data.

[0029] 4, 5 and 6 are diagrams for explaining the synchronization process between the first first period unit data and the second first period unit data in step S23, with the first first period unit data in FIG. 3 being the predetermined data. FIG. 4 is a diagram in which the second first period unit data is aligned by shifting the sample by j with respect to the first first period unit data. The range of j is -j max ≦j≦j max In Figure 4, the i-th sample of the first period unit data is A i , the i-th sample of the second first period unit data is B i Then, sample A i and Sample B i+j At this time, the difference Δ between the first first period unit data and the second first period unit data shifted by j samples is j is calculated by formula (1). In formula (1), if the number of samples of the first first period unit data and the number of samples of the second first period unit data are M, then m s ≧j max ,m f ≧Mj max is.

[0030]

number

[0031] Figure 5 shows the -j max ≦j≦jmax The difference Δ calculated by equation (1) for each integer j j In the example of FIG. max = 100. The synchronization process of the first first period unit data and the second first period unit data is performed by synchronizing the second first period unit data with the first first period unit data by the difference Δ j The diagnostic device 100 shifts the samples by the integer j that minimizes the first period unit data. max ≦j≦j max As a result, the difference between the first period unit data and the first period unit data is calculated as follows: j The series is calculated, and the Nth first period unit data is compared with the first first period unit data, and the difference Δ j 6 is a diagram in which waveforms of a plurality of first period unit data extracted from the first measurement data and subjected to synchronization processing are superimposed.

[0032] Fig. 7 is a diagram showing the waveform of reference data generated by calculating an average value as a representative value of the plurality of first period unit data synchronized in step S23. The waveform of the reference data in Fig. 7 is an averaged waveform obtained by averaging the waveforms of the plurality of first period unit data in Fig. 6. High frequency noise is reduced by averaging the waveforms of the plurality of first period unit data.

[0033] Fig. 8 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the reference data of Fig. 7. In the frequency spectrum shown in Fig. 8, a clear peak appears at a specific frequency due to the reduction of high-frequency noise, and it can be said that the reference data obtained by averaging multiple first-period unit data is data that clearly shows the state of the object in the first period.

[0034] FIG. 9 is a flowchart showing an example of the procedure of the diagnosis step S5 in FIG. 1. As shown in FIG. 9, first, in step S51, the diagnostic device 100 extracts diagnostic object data corresponding to at least a part of a predetermined motion pattern repeated by the object during a second time period from the second measurement data acquired in step S4 in FIG. 1. The process of extracting diagnostic object data from the second measurement data is similar to the process of extracting any first-period unit data from the first measurement data in step S21 in FIG. 2. For example, if the object repeats a predetermined motion pattern consisting of a first motion, a stop, a second motion, and a stop during the second time period, as in the first time period, the diagnostic device 100 may extract, from the second measurement data, data representing the kth time when the object performs the first motion, a stop, and the second motion, which are part of the predetermined motion pattern.

[0035] Then, in step S52, the diagnostic device 100 synchronizes the reference data generated in step S2 of FIG. 1 with the diagnostic object data extracted in step S51, and diagnoses the state of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization process is a process of aligning the timing so that the difference between the reference data and the diagnostic object data becomes the smallest. Specifically, the i-th sample of the reference data is synchronized with C i , the i-th sample of the diagnostic data is D i Then, the difference Δ between the reference data and the diagnostic data shifted by j samples j is calculated by the same formula (2) as the formula (1) above. In formula (2), the range of j is -j max ≦j≦j max Let the number of samples of the reference data and the number of samples of the diagnostic data be M, then m s ≧j max ,m f ≧Mj max is.

[0036]

number

[0037] The diagnostic device 100 sets the range of j to -jmax ≦j≦j max The difference Δ between the reference data and the diagnostic data is calculated using equation (2). j The series is calculated, and the diagnostic target data is compared with the reference data to determine the difference Δ j The sample is shifted by the integer j that minimizes the difference Δ j The minimum value of min{Δ j Then, the diagnostic device 100 calculates the minimum value min{Δ j} is smaller than a predetermined threshold, it can be diagnosed that the difference between the state of the object in the second period and the state of the object in the first period is small, that is, the change in the state of the object is small. j} is greater than a predetermined threshold, it can be diagnosed that there is a large difference between the state of the object in the second period and the state of the object in the first period, i.e., that there is a large change in the state of the object. If the first period is a predetermined period during which the object operates normally, the diagnostic device 100 determines the minimum value min{Δ j} is smaller than a predetermined threshold, the object in the second period is diagnosed as being in a normal state, and the minimum value min{Δ j If {} is greater than a predetermined threshold, it can be diagnosed that the object in the second period is in an abnormal state.

[0038] In step S23 of FIG. 2, the diagnostic device 100 calculates the minimum value min{Δ j} reference value ref{min{Δ j}}, and the difference Δ j The minimum value of min{Δ j} reference value ref{min{Δ j}} standard value std{min{Δ j}} may be compared with a predetermined threshold value to diagnose the state of the object in the second period. In this way, the minimum value min{Δ j Even if the size of} is different, the standard value std{min{Δ jSince the magnitude of the reference value ref{min{Δ j}}, and the minimum value min{Δ j} may be calculated.

[0039] In step S5 of FIG. 1, the diagnostic device 100 may perform other diagnostic processes in addition to or instead of the diagnostic process shown in FIG. 9. For example, the diagnostic device 100 may calculate the RMS values ​​of the reference data and the diagnostic object data, respectively, and compare the difference between the two RMS values ​​with a predetermined threshold to diagnose the condition of the object during the second period. Alternatively, for example, the diagnostic device 100 may perform a fast Fourier transform on the reference data and the diagnostic object data, respectively, to calculate two frequency spectra, and compare the difference in peak frequency or peak intensity with a predetermined threshold to diagnose the condition of the object during the second period. Alternatively, for example, if the physical quantity sensor has multiple detection axes, the diagnostic device 100 may generate reference data and diagnostic object data for each detection axis, calculate Lissajous figures for the multiple reference data and the multiple diagnostic object data, and diagnose the condition of the object during the second period based on the difference between the two Lissajous figures. FIG. 10 shows an example of a Lissajous figure. The example in FIG. 10 shows Lissajous figures for X-axis data and Y-axis data.

[0040] 1-1-2. Diagnostic equipment Fig. 11 is a diagram showing an example of the configuration of a diagnostic device 100 that executes the diagnostic method of the first embodiment. As shown in Fig. 11, the diagnostic device 100 includes a physical quantity sensor 200, an analog front-end 210, a processing circuit 110, a memory circuit 120, an operation unit 130, a display unit 140, a sound output unit 150, and a communication unit 160. Note that the diagnostic device 100 may be configured by omitting or modifying some of the components shown in Fig. 11 or by adding other components. For example, the physical quantity sensor 200 and the analog front-end 210 do not have to be components of the diagnostic device 100.

[0041] The physical quantity sensor 200 detects a physical quantity generated when the object repeats a predetermined movement pattern during the first period and the second period, and outputs a signal having a magnitude corresponding to the detected physical quantity. The output signal of the physical quantity sensor 200 is input to the analog front end 210.

[0042] The analog front end 210 performs amplification processing, A / D conversion processing, etc. on the output signal of the physical quantity sensor 200, and outputs a digital time-series signal.

[0043] The processing circuit 110 acquires a digital time-series signal output from the analog front-end 210 during a first period as first measurement data, and acquires a digital time-series signal output from the analog front-end 210 during a second period as second measurement data, and performs signal processing. Specifically, the processing circuit 110 executes a diagnostic program 121 stored in a memory circuit 120 and performs various calculations on the first measurement data and the second measurement data. The processing circuit 110 also performs various processes in response to operation signals from an operation unit 130, sending display signals to display various information on a display unit 140, sending sound signals to a sound output unit 150 to generate various sounds, and controlling a communication unit 160 to perform data communication with an external device (not shown). The processing circuit 110 is realized by, for example, a CPU or a DSP. CPU stands for Central Processing Unit, and DSP stands for Digital Signal Processor.

[0044] The processing circuit 110 executes the diagnostic program 121 to function as a first measurement data acquisition circuit 111, a reference data generation circuit 112, a second measurement data acquisition circuit 113, and a diagnostic circuit 114. That is, the diagnostic device 100 includes the first measurement data acquisition circuit 111, the reference data generation circuit 112, the second measurement data acquisition circuit 113, and the diagnostic circuit 114.

[0045] The first measurement data acquisition circuit 111 acquires first measurement data based on a time-series signal obtained by the physical quantity sensor 200 detecting a physical quantity generated by the object repeating a predetermined movement pattern during a first period. That is, the first measurement data acquisition circuit 111 acquires, as the first measurement data, a digital time-series signal output from the analog front-end 210 during the first period. That is, the first measurement data acquisition circuit 111 executes the first measurement data acquisition step S1 in FIG. 1 . The first measurement data acquired by the first measurement data acquisition circuit 111 is stored in the memory circuit 120.

[0046] The reference data generation circuit 112 generates reference data based on the first measurement data acquired by the first measurement data acquisition circuit 111. Specifically, the reference data generation circuit 112 extracts, from the first measurement data, a plurality of first period unit data each corresponding to at least a part of a predetermined movement pattern repeated by the object in a first period, performs synchronization processing on the extracted plurality of first period unit data, and generates reference data by calculating a representative value of the synchronized plurality of first period unit data. The synchronization processing is processing for aligning timing so that the difference between the predetermined data included in the plurality of first period unit data and each of the other data is minimized. For example, the reference data generation circuit 112 calculates the difference Δ between the predetermined data and each of the other data using the above-mentioned formula (1). j A series of the above is calculated, and each of the other data is calculated with respect to the predetermined data by the difference Δ j The reference data generating circuit 112 performs synchronization processing by shifting the samples by an integer j that minimizes j. The representative value is, for example, an average value or a median. The reference data generating circuit 112 may display the extracted plurality of first period unit data on the display unit 140. That is, the reference data generating circuit 112 executes the reference data generating step S2 in FIG. 1, specifically steps S21, S22, and S23 in FIG. 2. The reference data generated by the reference data generating circuit 112 is stored in the memory circuit 120.

[0047] The second measurement data acquisition circuit 113 acquires second measurement data based on a time-series signal obtained by the physical quantity sensor 200 detecting a physical quantity generated by the object repeating a predetermined movement pattern during the second period. That is, the second measurement data acquisition circuit 113 acquires, as the second measurement data, a digital time-series signal output from the analog front-end 210 during the second period. That is, the second measurement data acquisition circuit 113 executes the second measurement data acquisition step S4 in FIG. 1 . The second measurement data acquired by the second measurement data acquisition circuit 113 is stored in the memory circuit 120.

[0048] The diagnostic circuit 114 diagnoses the condition of the object based on the reference data generated by the reference data generation circuit 112 and the second measurement data acquired by the second measurement data acquisition circuit 113. The diagnostic circuit 114 may diagnose whether the object is in a normal or abnormal state during the second period, assuming that the object is in a normal state during the first period. The diagnostic device 100 may also diagnose the extent to which the object has changed over time from the first period to the second period. For example, the diagnostic circuit 114 may extract diagnostic object data from the second measurement data corresponding to at least a portion of a predetermined motion pattern repeated by the object during the second period, synchronize the reference data with the diagnostic object data, and diagnose the condition of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization process is a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data.

[0049] For example, the diagnostic circuitry 114 may set the range of j to -j max ≦j≦j max The difference Δ between the reference data and the diagnostic data is calculated using equation (2). j The series is calculated, and the diagnostic target data is compared with the reference data to determine the difference Δ j The sample is shifted by the integer j that minimizes the difference Δ j The minimum value of min{Δ j Then, the diagnostic circuit 114 calculates the minimum value min{Δ j} is smaller than a predetermined threshold, it can be diagnosed that the difference between the state of the object in the second period and the state of the object in the first period is small, that is, the change in the state of the object is small. j} is greater than a predetermined threshold, it can be diagnosed that there is a large difference between the state of the object in the second period and the state of the object in the first period, i.e., that there is a large change in the state of the object. If the first period is a predetermined period during which the object operates normally, the diagnosis circuit 114 determines the minimum value min{Δ j} is smaller than a predetermined threshold, the object in the second period is diagnosed as being in a normal state, and the minimum value min{Δ j} is greater than a predetermined threshold, the object in the second period can be diagnosed as being in an abnormal state. That is, the diagnostic circuit 114 executes the diagnostic step S5 in Fig. 1, specifically, steps S51 and S52 in Fig. 9. Information on the diagnosis result of the diagnostic circuit 114 is stored in the memory circuit 120.

[0050] The diagnostic circuit 114 may perform other diagnostic processes. For example, the diagnostic circuit 114 may calculate the RMS values ​​of the reference data and the diagnostic object data, and compare the difference between the two RMS values ​​with a predetermined threshold to diagnose the condition of the object in the second period. Alternatively, for example, the diagnostic circuit 114 may perform a fast Fourier transform on the reference data and the diagnostic object data to calculate two frequency spectra, and compare the difference between the peak frequencies or the peak intensities with a predetermined threshold to diagnose the condition of the object in the second period. Alternatively, for example, if the physical quantity sensor 200 has multiple detection axes, the diagnostic circuit 114 may generate reference data and diagnostic object data for each detection axis, calculate Lissajous figures for the multiple reference data and Lissajous figures for the multiple diagnostic object data, and diagnose the condition of the object in the second period based on the difference between the two Lissajous figures.

[0051] The storage circuitry 120 has a ROM and a RAM (not shown). ROM stands for Read Only Memory, and RAM stands for Random Access Memory. The ROM stores various programs such as the diagnostic program 121 and predetermined data, and the RAM stores data generated by the processing circuitry 110. The RAM is also used as a working area for the processing circuitry 110, and stores programs and data read from the ROM, data input from the operation unit 130, and data temporarily generated by the processing circuitry 110.

[0052] The operation unit 130 is an input device configured with operation keys, button switches, etc., and outputs an operation signal to the processing circuit 110 in response to an operation by a user.

[0053] The display unit 140 is a display device configured with an LCD or the like, and displays various information based on a display signal output from the processing circuit 110. LCD is an abbreviation for Liquid Crystal Display. The display unit 140 may be provided with a touch panel that functions as the operation unit 130. For example, based on the display signal output from the processing circuit 110, the display unit 140 may display a screen including at least a portion of the first measurement data, a plurality of first period unit data, reference data, second measurement data, diagnostic object data, diagnostic result information, and a Lissajous figure.

[0054] The sound output unit 150 is configured with a speaker or the like, and generates various sounds based on the sound signal output from the processing circuit 110. For example, the sound output unit 150 may generate sounds indicating the start or end of a diagnostic process based on the sound signal output from the processing circuit 110.

[0055] The communication unit 160 performs various controls to establish data communication between the processing circuit 110 and an external device. For example, the communication unit 160 transmits information including at least a part of the first measurement data, the plurality of first period unit data, the reference data, the second measurement data, the diagnostic object data, the diagnostic result information, and the Lissajous figure to the external device, and the external device may display at least a part of the received information on a display unit (not shown).

[0056] At least some of the first measurement data acquisition circuit 111, the reference data generation circuit 112, the second measurement data acquisition circuit 113, and the diagnosis circuit 114 may be implemented by dedicated hardware. The diagnostic device 100 may be a single device or may be configured by multiple devices. For example, the physical quantity sensor 200 and the analog front-end 210 may be included in a first device, and the processing circuit 110, the memory circuit 120, the operation unit 130, the display unit 140, the sound output unit 150, and the communication unit 160 may be included in a second device separate from the first device. Alternatively, for example, the processing circuit 110 and the memory circuit 120 may be implemented by a device such as a cloud server, which generates multiple pieces of first period unit data, reference data, diagnostic object data, information on diagnostic results, and information such as Lissajous figures, and transmits the generated information to a terminal including the operation unit 130, the display unit 140, the sound output unit 150, and the communication unit 160 via a communication line.

[0057] 1-1-3.Effects In the diagnostic method of the first embodiment described above, the diagnostic device 100 acquires first measurement data based on physical quantities generated when an object repeats a predetermined movement pattern in a first period, and generates reference data by synchronously processing a plurality of first period unit data extracted from the first measurement data and calculating a representative value. Therefore, according to the diagnostic method of the first embodiment, the diagnostic device 100 can improve the reliability of the diagnosis by diagnosing the condition of the object based on highly accurate reference data in which the variance of the plurality of first period unit data is reduced. In particular, the diagnostic device 100 can generate highly accurate reference data in which the variance and high-frequency noise of the plurality of first period unit data is reduced by calculating the average value of the synchronously processed plurality of first period unit data, thereby further improving the reliability of the diagnosis.

[0058] Furthermore, in the diagnostic method of the first embodiment, the synchronization process of the plurality of first period unit data is a process of aligning the timing so as to minimize the difference between predetermined data included in the plurality of first period unit data and each of the other data, so although the calculation load of the synchronization process is large, the plurality of first period unit data is accurately synchronized. Therefore, according to the diagnostic method of the first embodiment, the accuracy of the reference data is improved, and the reliability of the condition diagnosis of the target object can be increased.

[0059] Furthermore, in the diagnostic method of the first embodiment, the diagnostic device 100 acquires second measurement data based on physical quantities generated by the object repeating a predetermined motion pattern during a second time period, synchronizes the reference data with diagnostic object data extracted from the second measurement data, and diagnoses the state of the object based on the difference between the synchronized reference data and the diagnostic object data. Therefore, according to this diagnostic method, regardless of the type of object or the type of predetermined motion pattern repeatedly performed by the object, the greater the change in the state of the object between the first time period and the second time period, the greater the difference between the reference data and the diagnostic object data, making it possible to realize a highly versatile and easy-to-use diagnosis based on the difference.

[0060] 1-2. Second embodiment In the following, in the second embodiment, the same components as those in the first embodiment are given the same reference numerals, and explanations that overlap with those in the first embodiment are omitted or simplified, and differences from the first embodiment are mainly described.

[0061] The flowchart showing the procedure of the diagnostic method of the second embodiment is the same as that of Fig. 1, and is therefore omitted from the illustration. In the diagnostic method of the second embodiment, the processing of the first measurement data acquisition step S1 and the second measurement data acquisition step S4 is the same as that of the first embodiment. In the diagnostic method of the second embodiment, the method of synchronization processing in step S23 of the reference data generation step S2 and step S52 of the diagnostic step S5 is different from that of the first embodiment.

[0062] In the diagnostic method of the second embodiment, the synchronization process of the plurality of first period unit data in step S23 of the reference data generating step S2 is a process of making the timing at which the amplitude of predetermined data included in the plurality of first period unit data becomes maximum coincide with the timing at which the amplitude of each of the other data becomes maximum. For example, the diagnostic device 100 performs synchronization process of shifting samples by an integer j with respect to the predetermined data so that the timings at which the amplitudes of both data become maximum coincide.

[0063] Fig. 12 is a diagram showing a portion of the first measurement data acquired in the first measurement data acquisition step S1. The first measurement data shown in Fig. 12 is a portion of speed data based on a time-series signal output from a speed sensor, which is a physical quantity sensor, when a motor, which is an object, repeats a predetermined operation pattern consisting of a first operation, a stop, a second operation, and a stop during a first period. As shown in Fig. 12, in step S21 of the reference data generation step S2, the diagnosis device 100 extracts, from the first measurement data, data when the motor performs the first operation, a stop, and a second operation, which are part of the predetermined operation pattern, for the nth time, as the nth first period unit data.

[0064] 13 and 14 are diagrams for explaining the synchronization process between the first first period unit data and the second first period unit data in step S23 of the reference data generation step S2, with the first first period unit data in FIG. 12 being the predetermined data. FIG. 13 is a diagram in which the second first period unit data is aligned by shifting the sample by j with respect to the first first period unit data. In FIG. 13, the i-th sample of the first first period unit data is A i , the i-th sample of the second first period unit data is B i Then, sample A i and Sample B i+j will correspond to each other at the same timing. In this case, determining the integer j so that the timing at which the amplitude of the first first period unit data is maximum and the timing at which the amplitude of the second first period unit data is maximum coincides corresponds to synchronization processing between the first first period unit data and the second first period unit data. The timing at which the amplitude is maximum is the timing of the maximum value when the maximum value of the first period unit data is greater than the absolute value of the minimum value, and is the timing of the minimum value when the maximum value of the first period unit data is smaller than the absolute value of the minimum value. In the example of FIG. 13, the timing at which the amplitude of the first first period unit data is maximum is the timing of the minimum value. Similarly, the timing at which the amplitude of the second first period unit data is maximum is the timing of the minimum value.

[0065] The diagnostic device 100 performs synchronization processing to determine the integer j for the second and subsequent N-th first period unit data so that the timing at which the amplitude of the first first period unit data becomes maximum coincides with the timing at which the amplitude of the N-th first period unit data becomes maximum. Fig. 14 is a diagram in which waveforms of a plurality of first period unit data extracted from the first measurement data and synchronized are superimposed.

[0066] Fig. 15 is a diagram showing the waveform of reference data generated by calculating an average value as a representative value of a plurality of first period unit data synchronized in step S23 of the reference data generation step S2. The waveform of the reference data in Fig. 15 is an averaged waveform obtained by averaging the waveforms of the plurality of first period unit data in Fig. 14. High frequency noise is reduced by averaging the waveforms of the plurality of first period unit data.

[0067] Similarly, the synchronization process between the reference data and the diagnostic object data in step S52 of the diagnostic process S5 is a process for matching the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized. For example, the diagnostic device 100 performs synchronization process for shifting the samples of the diagnostic object data relative to the reference data by an integer j so that the timings at which the amplitudes of both the reference data and the diagnostic object data are maximized are matched.

[0068] For example, the diagnostic device 100 calculates the difference Δ between the synchronized reference data and the diagnostic object data using the above-mentioned formula (2). j Calculate the difference Δ j If the difference Δ is smaller than the predetermined threshold, it can be determined that the difference between the state of the object in the second period and the state of the object in the first period is small, that is, the change in the state of the object is small. j If the difference Δ is greater than the predetermined threshold, it can be diagnosed that the difference between the state of the object in the second period and the state of the object in the first period is large, i.e., the state change of the object is large. If the first period is a predetermined period during which the object operates normally, the diagnostic device 100 determines that the difference Δ j If the difference Δ j If the difference is greater than a predetermined threshold, the object in the second period can be diagnosed as being in an abnormal state.

[0069] In step S23 of FIG. 2, the diagnostic device 100 calculates the minimum value min{Δ j} reference value ref{min{Δ j}}, and the difference Δ j The minimum value of min{Δ j} reference value ref{min{Δ j}} standard value std{min{Δ j}} may be compared with a predetermined threshold value to diagnose the state of the object in the second period. In this way, the minimum value min{Δ j Even if the size of} is different, the standard value std{min{Δ j Since the magnitude of the reference value ref{min{Δ j}}, and the minimum value min{Δ j} may be calculated.

[0070] An example of the configuration of the diagnostic device 100 that executes the diagnostic method of the second embodiment is the same as that shown in Fig. 11, and therefore is not shown in the figure. However, in the second embodiment, the processes performed by the reference data generation circuit 112 and the diagnostic circuit 114 are different from those in the first embodiment.

[0071] The reference data generation circuit 112 generates reference data based on the first measurement data acquired by the first measurement data acquisition circuit 111. Specifically, the reference data generation circuit 112 extracts, from the first measurement data, a plurality of first period unit data corresponding to at least a portion of a predetermined motion pattern repeated by the object during a first period, performs synchronization processing on the extracted plurality of first period unit data, and calculates a representative value of the synchronized plurality of first period unit data to generate reference data. The synchronization processing is processing for matching the timing at which the amplitude of a predetermined data included in the plurality of first period unit data becomes maximum with the timing at which the amplitude of each of the other data becomes maximum. For example, the reference data generation circuit 112 performs synchronization processing by shifting samples of the predetermined data by an integer j so that the timing at which the amplitudes of both data become maximum coincides with each other. The representative value may be, for example, an average value or a median value. The reference data generation circuit 112 may display the extracted plurality of first period unit data on the display unit 140. That is, the reference data generation circuit 112 executes the reference data generation step S2 in Fig. 1, specifically steps S21, S22, and S23 in Fig. 2. The reference data generated by the reference data generation circuit 112 is stored in the storage circuit 120.

[0072] The diagnostic circuit 114 diagnoses the condition of the object based on the reference data generated by the reference data generation circuit 112 and the second measurement data acquired by the second measurement data acquisition circuit 113. The diagnostic circuit 114 may diagnose whether the object is in a normal or abnormal state during the second period, assuming that the object is in a normal state during the first period. The diagnostic device 100 may also diagnose the extent to which the object has changed over time from the first period to the second period. For example, the diagnostic circuit 114 may extract diagnostic object data from the second measurement data corresponding to at least a portion of a predetermined motion pattern repeated by the object during the second period, synchronize the reference data with the diagnostic object data, and diagnose the condition of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization process is a process of matching the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized. For example, the diagnostic circuit 114 performs synchronization processing to align the diagnostic target data with the reference data by shifting the samples by an integer j so that the timings at which the amplitudes of both data become maximum coincide with each other.

[0073] For example, the diagnostic circuit 114 calculates the difference Δ between the synchronized reference data and the diagnostic target data using the above-mentioned equation (2). j Calculate the difference Δ j If the difference Δ is smaller than the predetermined threshold, it can be determined that the difference between the state of the object in the second period and the state of the object in the first period is small, i.e., the change in the state of the object is small. j If the difference Δ is greater than the predetermined threshold, it can be diagnosed that the difference between the state of the object in the second period and the state of the object in the first period is large, i.e., the state change of the object is large. If the first period is a predetermined period during which the object operates normally, the diagnosis circuit 114 determines that the difference Δ j If the difference Δ jis greater than a predetermined threshold, the object in the second period can be diagnosed as being in an abnormal state. That is, the diagnostic circuit 114 executes the diagnostic step S5 in Fig. 1, specifically, steps S51 and S52 in Fig. 9. Information on the diagnostic result of the diagnostic circuit 114 is stored in the memory circuit 120.

[0074] Other configurations of the diagnostic device 100 in the second embodiment are the same as those in the first embodiment, and therefore description thereof will be omitted.

[0075] As described above, according to the diagnostic method of the second embodiment, the synchronization process of the plurality of first period unit data is a process for matching the timing at which the amplitude of predetermined data included in the plurality of first period unit data becomes maximum with the timing at which the amplitude of each of the other data becomes maximum, and therefore the calculation load of the synchronization process is small. Furthermore, according to the diagnostic method of the second embodiment, when the object repeats an operation pattern in which the amplitude of a detected physical quantity becomes maximum at a predetermined timing, the plurality of first period unit data are accurately synchronized, thereby improving the accuracy of the reference data and increasing the reliability of the condition diagnosis of the object.

[0076] In addition, according to the diagnostic method of the second embodiment, the same effects as those of the diagnostic method of the first embodiment can be obtained.

[0077] 1-3. Third embodiment Hereinafter, for the third embodiment, components similar to those in the first or second embodiment will be given the same symbols, explanations that overlap with those in the first or second embodiment will be omitted or simplified, and the following will mainly describe the differences from the first and second embodiments.

[0078] The flowchart showing the procedure of the diagnostic method of the third embodiment is the same as that shown in FIG. 1, and is therefore not shown. In the diagnostic method of the third embodiment, the processing of the first measurement data acquisition step S1 and the second measurement data acquisition step S4 is the same as that of the first or second embodiment. In the diagnostic method of the third embodiment, the processing of the diagnostic step S5 is different from that of the first or second embodiment.

[0079] FIG. 16 is a flowchart showing an example of the procedure of the diagnosis step S5 in the diagnosis method of the third embodiment. As shown in FIG. 16, first, in step S53, the diagnosis device 100 extracts, from the second measurement data acquired in the second measurement data acquisition step S4, a plurality of second period unit data corresponding to at least a part of a predetermined movement pattern repeated by the object during the second period. For example, if the predetermined movement pattern is a pattern in which the object performs one type of movement and then stops the movement, each of the plurality of second period unit data may be data corresponding to that movement. Also, for example, if the predetermined movement pattern is a pattern in which the object stops the movement after performing each of a plurality of different types of movement, each of the plurality of second period unit data may be data corresponding to the plurality of movements.

[0080] The processing in step S53 is the same as the processing in step S21 in FIG. 2 in which the first measurement data is replaced with the second measurement data and the plurality of first period unit data is replaced with the plurality of second period unit data.

[0081] Next, in step S54, the diagnostic device 100 performs synchronization processing on the plurality of second period unit data extracted in step S53, and generates diagnostic target data by calculating a representative value of the plurality of synchronized second period unit data. As in the first embodiment, the synchronization processing may be processing for aligning the timing so that the difference between predetermined data included in the plurality of second period unit data and each of the other data is minimized. Specifically, the i-th sample of the predetermined data is taken as E i , the i-th sample of any other data F i Then, the difference Δ between the given data and other data shifted by j samples is j is calculated by the same formula (3) as the above formula (1). In formula (3), the range of j is -j max ≦j≦j max Let the number of samples of the given data and the number of samples of other data be M, then m s ≧j max ,m f ≧Mj max is.

[0082]

number

[0083] Alternatively, similar to the second embodiment, the synchronization process may be a process that ensures that the timing at which the amplitude of a specified piece of data included in a plurality of second period unit data reaches its maximum coincides with the timing at which the amplitude of each of the other data reaches its maximum.

[0084] The representative value is, for example, an average value, a median value, etc. For example, if the representative value is an average value, diagnostic object data in which high frequency noise has been reduced is generated by averaging the waveforms of a plurality of second period unit data.

[0085] The processing in step S54 is the same as the processing in step S23 in FIG. 2 in which the plurality of first period unit data are replaced with the plurality of second period unit data and the reference data are replaced with the diagnostic object data.

[0086] Then, in step S55, diagnostic device 100 synchronizes the reference data generated in the reference data generating step S2 with the diagnostic object data generated in step S54, and diagnoses the condition of the object based on the difference between the synchronized reference data and the diagnostic object data. As in the first embodiment, the synchronization may be a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data. Alternatively, as in the second embodiment, the synchronization may be a process of aligning the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized.

[0087] An example of the configuration of the diagnostic device 100 that executes the diagnostic method of the third embodiment is the same as that shown in Fig. 11, and therefore is not shown in the figure. However, in the third embodiment, the processing performed by the diagnostic circuit 114 is different from that in the first and second embodiments.

[0088] The diagnostic circuit 114 diagnoses the condition of the object based on the reference data generated by the reference data generation circuit 112 and the second measurement data acquired by the second measurement data acquisition circuit 113. Specifically, first, the diagnostic circuit 114 extracts, from the second measurement data acquired by the second measurement data acquisition circuit 113, a plurality of second period unit data corresponding to at least a part of a predetermined motion pattern repeated by the object during a second period. Next, the diagnostic circuit 114 synchronizes the extracted plurality of second period unit data and calculates a representative value of the synchronized plurality of second period unit data to generate diagnostic object data. The synchronization process may be a process of aligning the timing so as to minimize the difference between the predetermined data included in the plurality of second period unit data and each of the other data, or a process of aligning the timing at which the amplitude of the predetermined data included in the plurality of second period unit data is maximized with the timing at which the amplitude of each of the other data is maximized. The representative value may be, for example, an average value or a median value. The diagnostic circuit 114 then synchronizes the reference data with the diagnostic object data and diagnoses the condition of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization may be a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data, or a process of aligning the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized. That is, the diagnostic circuit 114 executes the diagnostic step S5 in FIG. 1, specifically steps S53, S54, and S55 in FIG. 16. Information on the diagnosis results of the diagnostic circuit 114 is stored in the memory circuit 120.

[0089] Other configurations of the diagnostic device 100 in the third embodiment are the same as those in the first or second embodiment, and therefore description thereof will be omitted.

[0090] As described above, in the diagnostic method of the third embodiment, the diagnostic device 100 acquires second measurement data based on physical quantities generated when the object repeats a predetermined movement pattern in a second time period, and generates diagnostic object data by synchronously processing a plurality of second period unit data extracted from the second measurement data and calculating a representative value. Therefore, according to the diagnostic method of the third embodiment, the diagnostic device 100 can improve the reliability of the diagnosis by diagnosing the condition of the object based on highly accurate diagnostic object data in which the variance of the plurality of second period unit data is reduced. In particular, the diagnostic device 100 can calculate an average value of the synchronously processed plurality of second period unit data to generate highly accurate diagnostic object data in which the variance and high-frequency noise of the plurality of second period unit data are reduced, thereby further improving the reliability of the diagnosis.

[0091] Additionally, according to the diagnostic method of the third embodiment, the same effects as those of the diagnostic method of the first or second embodiment can be obtained.

[0092] 1-4. Fourth embodiment Hereinafter, for the fourth embodiment, components similar to those of any of the first to third embodiments will be given the same symbols, and explanations that overlap with those of any of the first to third embodiments will be omitted or simplified, and the following will mainly describe the differences from any of the first to third embodiments.

[0093] In the diagnostic methods of the first to third embodiments, when a predetermined motion pattern is repeated in which an object stops each of a plurality of different motions during a first period, variations in the stop time may reduce the accuracy of the reference data. For example, if the stop time between the first and second motions in the first measurement data shown in FIG. 3 varies, the timing of at least one of the sample groups corresponding to the first motion and the sample groups corresponding to the second motion will vary in the synchronized plurality of first period unit data, reducing the accuracy of the calculated representative value. Therefore, in the diagnostic method of the fourth embodiment, the diagnostic device 100 separately generates a plurality of first period unit data, performs synchronization processing, and generates reference data for each of the plurality of motions included in the predetermined motion pattern. This prevents variations in the stop time from reducing the accuracy of the reference data corresponding to each motion.

[0094] The flowchart showing the procedure of the diagnostic method of the fourth embodiment is the same as that shown in FIG. 1, and is therefore not shown. In the diagnostic method of the fourth embodiment, the processing of the first measurement data acquisition step S1 and the second measurement data acquisition step S4 is the same as that of the first to third embodiments. In the diagnostic method of the fourth embodiment, the processing of the reference data generation step S2 and the processing of the diagnostic step S5 are different from those of the first to third embodiments.

[0095] Fig. 17 is a flowchart showing an example of the procedure of the reference data generation step S2 in the fourth embodiment. Note that Fig. 17 shows an example in which the predetermined movement pattern repeated by the object during the first period is a pattern in which the object stops moving after performing each of the first to Nth movements of different types, where N is an integer of 2 or greater.

[0096] As shown in FIG. 17, first, in step S201, the diagnostic device 100 sets the integer i to 1, and in step S202, extracts a plurality of first period unit data corresponding to the i-th movement included in a predetermined movement pattern from the first measurement data acquired in the first measurement data acquisition step S1.

[0097] Next, in step S203, the diagnostic device 100 displays the plurality of first period unit data corresponding to the i-th action extracted in step S202 on a display unit (not shown).

[0098] Next, in step S204, the diagnostic device 100 performs synchronization processing on the plurality of first period unit data corresponding to the i-th action extracted in step S202, and generates reference data corresponding to the i-th action by calculating a representative value of the synchronized plurality of first period unit data. The synchronization processing may be processing that aligns the timing so as to minimize the difference between predetermined data included in the plurality of first period unit data and each of the other data, or processing that matches the timing at which the amplitude of predetermined data included in the plurality of first period unit data is maximized with the timing at which the amplitude of each of the other data is maximized. The representative value may be, for example, an average value or a median value.

[0099] If the integer i is not N in step S205, diagnostic device 100 increments the integer i by 1 in step S206 and repeats step S202 and subsequent steps, and if the integer i is N in step S205, ends the process.

[0100] FIG. 18 is a diagram illustrating an example of the relationship between first measurement data and multiple first period unit data corresponding to an i-th operation. The first measurement data illustrated in FIG. 18 is a portion of speed data based on a time-series signal output from a speed sensor, which is a physical quantity sensor, when a motor, which is an object, repeats a predetermined operation pattern consisting of a first operation, a stop, a second operation, and a stop during a first period. As illustrated in FIG. 18, in step S202, the diagnostic device 100 extracts data from the first measurement data when the motor performs the first operation for the nth time as the n-th first period unit data corresponding to the first operation, and in step S204, generates reference data corresponding to the first operation. In addition, in step S202, the diagnostic device 100 extracts data from the first measurement data when the motor performs the second operation for the n-th time as the n-th first period unit data corresponding to the second operation, and in step S204, generates reference data corresponding to the second operation.

[0101] Fig. 19 is a flowchart showing an example of the procedure of the diagnosis step S5 in the fourth embodiment. As shown in Fig. 19, first, in step S501, the diagnosis device 100 sets the integer i to 1, and in step S502, extracts diagnosis target data corresponding to the i-th movement included in each predetermined movement pattern from the second measurement data acquired in the second measurement data acquisition step S4.

[0102] Next, in step S503, diagnostic device 100 synchronizes the reference data corresponding to the ith action generated in reference data generating step S2 with the diagnostic object data extracted in step S502, and diagnoses the state of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization may be a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data, or a process of aligning the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized.

[0103] If the integer i is not N in step S504, diagnostic device 100 increments the integer i by 1 in step S505 and repeats step S502 and subsequent steps, and if the integer i is N in step S504, ends the processing.

[0104] Fig. 20 is a flowchart showing another example of the procedure of the diagnosis step S5 in the diagnosis method of the fourth embodiment. As shown in Fig. 20, first, in step S511, the diagnosis device 100 sets the integer i to 1, and in step S512, extracts a plurality of second period unit data corresponding to the i-th movement included in the predetermined movement pattern from the second measurement data acquired in the second measurement data acquisition step S4.

[0105] Next, in step S513, the diagnostic device 100 synchronizes the plurality of second period unit data corresponding to the i-th operation extracted in step S512, and generates diagnostic target data corresponding to the i-th operation by calculating a representative value of the synchronized plurality of second period unit data. The synchronization may be a process of aligning the timing so as to minimize the difference between predetermined data included in the plurality of second period unit data and each of the other data, or a process of aligning the timing at which the amplitude of predetermined data included in the plurality of second period unit data is maximized with the timing at which the amplitude of each of the other data is maximized. The representative value may be, for example, an average value or a median value.

[0106] Next, in step S514, diagnostic device 100 synchronizes the reference data corresponding to the ith action generated in the reference data generating step S2 with the diagnostic object data corresponding to the ith action generated in step S513, and diagnoses the state of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization may be a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data, or a process of aligning the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized.

[0107] If the integer i is not N in step S515, diagnostic device 100 increments the integer i by 1 in step S516 and repeats step S512 and subsequent steps, and if the integer i is N in step S515, ends the process.

[0108] An example of the configuration of the diagnostic device 100 that executes the diagnostic method of the fourth embodiment is the same as that shown in Fig. 11, and therefore is not shown in the figure. However, in the fourth embodiment, the processing performed by the reference data generation circuit 112 and the diagnostic circuit 114 is different from that in the first embodiment.

[0109] The reference data generation circuit 112 generates reference data corresponding to the i-th motion based on the first measurement data acquired by the first measurement data acquisition circuit 111 for each integer i between 1 and N, inclusive. N is an integer greater than or equal to 2. Specifically, for each integer i between 1 and N, inclusive, the reference data generation circuit 112 extracts, from the first measurement data, a plurality of first period unit data corresponding to the i-th motion included in a predetermined motion pattern repeated by the object during a first period, performs synchronization processing on the plurality of first period unit data corresponding to the extracted i-th motion, and calculates a representative value of the synchronized plurality of first period unit data to generate reference data corresponding to the i-th motion. The synchronization processing may be processing that aligns the timing of the predetermined data included in the plurality of first period unit data so as to minimize the difference between each of the predetermined data and the other data, or processing that aligns the timing at which the amplitude of the predetermined data included in the plurality of first period unit data becomes maximum with the timing at which the amplitude of each of the other data becomes maximum. The representative value may be, for example, an average value or a median value. The reference data generating circuit 112 may display a plurality of first period unit data corresponding to the extracted ith operation on the display unit 140. That is, the reference data generating circuit 112 executes the reference data generating step S2 in Fig. 1, specifically, steps S201 to S206 in Fig. 17. Each piece of reference data corresponding to the first operation to the Nth operation generated by the reference data generating circuit 112 is stored in the storage circuit 120.

[0110] The diagnostic circuit 114 diagnoses the state of the object for each integer i between 1 and N, based on the reference data corresponding to the i-th operation generated by the reference data generation circuit 112 and the second measurement data acquired by the second measurement data acquisition circuit 113. The diagnostic circuit 114 may diagnose whether the object is in a normal state or an abnormal state in the second period, assuming that the object is in a normal state in the first period. Furthermore, the diagnostic device 100 may diagnose how much the object has changed over time from the first period to the second period.

[0111] For example, the diagnostic circuit 114 may extract, from the second measurement data, diagnostic object data corresponding to an ith motion included in a predetermined motion pattern repeated by the object during a second period, for each integer i between 1 and N inclusive. Alternatively, for example, the diagnostic circuit 114 may extract, from the second measurement data, a plurality of second period unit data corresponding to the ith motion included in each predetermined motion pattern, for each integer i between 1 and N inclusive, synchronize the plurality of second period unit data corresponding to the extracted ith motion, and calculate a representative value of the synchronized plurality of second period unit data to generate diagnostic object data corresponding to the ith motion. The synchronization process may be a process of aligning the timing of the predetermined data included in the plurality of second period unit data to minimize the difference between each of the predetermined data and the other data, or a process of aligning the timing at which the amplitude of the predetermined data included in the plurality of second period unit data becomes maximum with the timing at which the amplitude of each of the other data becomes maximum. The representative value may be, for example, an average value or a median value.

[0112] The diagnostic circuit 114 may synchronize the reference data corresponding to the i-th operation with the diagnostic object data corresponding to the i-th operation for each integer i between 1 and N, and diagnose the condition of the object based on the difference between the synchronized reference data and the diagnostic object data. The synchronization may be a process of aligning the timing so as to minimize the difference between the reference data and the diagnostic object data, or a process of aligning the timing at which the amplitude of the reference data is maximized with the timing at which the amplitude of the diagnostic object data is maximized. That is, the diagnostic circuit 114 executes the diagnostic step S5 in FIG. 1, specifically, steps S501 to S505 in FIG. 19 or steps S511 to S516 in FIG. 20. Information on the diagnosis results of the diagnostic circuit 114 is stored in the memory circuit 120.

[0113] Other configurations of the diagnostic device 100 in the fourth embodiment are the same as those in the first embodiment, and therefore description thereof will be omitted.

[0114] As described above, in the diagnostic method of the fourth embodiment, the predetermined motion pattern repeatedly performed by the object during the first and second periods is a pattern in which the object stops motion after performing each of a plurality of different motions. Furthermore, each of the plurality of first period unit data extracted by the diagnostic device 100 in step S202 of the reference data generation step S2 corresponds to one of the plurality of motions. The diagnostic device 100 then generates a plurality of first period unit data corresponding to the i-th motion by repeating step S202 for each integer i between 1 and N. Since the plurality of first period unit data corresponding to the i-th motion generated in this manner do not include samples corresponding to motions other than the i-th motion, even if there is variation in the time at which the object stops motion before and after the i-th motion, the diagnostic device 100 can accurately synchronize the plurality of first period unit data corresponding to the i-th motion in step S204 to generate reference data corresponding to the i-th motion with high accuracy. Therefore, according to the diagnostic method of the fourth embodiment, the diagnostic device 100 can improve the reliability of the diagnosis by diagnosing the condition of the object based on the reference data corresponding to the i-th motion and the diagnostic target data.

[0115] In addition, according to the diagnostic method of the fourth embodiment, the same effects as those of the diagnostic methods of the first to third embodiments can be obtained.

[0116] 2. Diagnostic System In the following, for the diagnostic system of this embodiment, components similar to those described in any of the above embodiments will be given the same symbols, and explanations that overlap with any of the above embodiments will be omitted or simplified, with the main focus being on the differences from any of the above embodiments.

[0117] 21 is a diagram showing an example of the configuration of a diagnostic system of this embodiment. As shown in FIG. 21, the diagnostic system 10 of this embodiment includes a physical quantity sensor 200, an analog front-end 210, a diagnostic device 100, and a display device 220.

[0118] The object 1 includes a movable body 2 and a housing 3 that houses the movable body 2. The physical quantity sensor 200 is attached to the housing 3, detects a physical quantity generated when the object repeats a predetermined movement pattern during a first period and a second period, and outputs a signal having a magnitude corresponding to the detected physical quantity. The output signal of the physical quantity sensor 200 is input to an analog front end 210.

[0119] The analog front end 210 performs amplification processing, A / D conversion processing, etc. on the output signal of the physical quantity sensor 200, and outputs a digital time-series signal.

[0120] The diagnostic device 100 acquires, as first measurement data, a digital time-series signal output from the analog front-end 210 in a first period, and generates reference data based on the acquired first measurement data. The diagnostic device 100 also acquires, as second measurement data, a digital time-series signal output from the analog front-end 210 in a second period. The diagnostic device 100 then diagnoses the condition of the object based on the reference data and the second measurement data, and displays information on the diagnosis result on the display device 220. As the diagnostic device 100, for example, any of the diagnostic devices 100 of the first to fourth embodiments described above can be applied.

[0121] According to the diagnostic system 10 of this embodiment, the diagnostic device 100 can improve the reliability of the state diagnosis of the object 1.

[0122] The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

[0123] The above-described embodiment and modifications are merely examples, and the present invention is not limited to these. For example, the embodiments and modifications can be combined as appropriate.

[0124] The present invention includes configurations that are substantially the same as the configurations described in the embodiments, for example, configurations with the same functions, methods, and results, or configurations with the same purpose and effects. The present invention also includes configurations that replace non-essential parts of the configurations described in the embodiments. The present invention also includes configurations that achieve the same effects or purposes as the configurations described in the embodiments. The present invention also includes configurations that add publicly known technology to the configurations described in the embodiments.

[0125] The following can be derived from the above-described embodiment and modifications.

[0126] One aspect of the diagnostic method comprises: a first measurement data acquisition step of acquiring first measurement data based on a time-series signal obtained by detecting a physical quantity generated by the object repeating a predetermined movement pattern during a first period using a physical quantity sensor; a reference data generating step of generating reference data based on the first measurement data; a second measurement data acquisition step of acquiring second measurement data based on a time-series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeating the predetermined movement pattern during a second period; a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data; Including, The reference data generating step includes: extracting a plurality of first period unit data from the first measurement data, each of which corresponds to at least a part of the predetermined movement pattern; a step of performing a synchronization process on the plurality of first period unit data and calculating a representative value of the plurality of first period unit data that have been synchronized to generate the reference data; Includes.

[0127] According to this diagnostic method, first measurement data based on physical quantities generated when an object repeats a predetermined movement pattern during a first period is acquired, and multiple first period unit data extracted from the first measurement data are processed synchronously to calculate a representative value. This makes it possible to generate highly accurate reference data in which the variation in the multiple first period unit data is reduced, thereby improving the reliability of the condition diagnosis of the object.

[0128] In one embodiment of the diagnostic method, The reference data generating step includes: The method may include the step of displaying waveforms of the plurality of first period unit data.

[0129] In one embodiment of the diagnostic method, The synchronization process may be a process of aligning timing so as to minimize a difference between predetermined data included in the plurality of first period unit data and each of other data.

[0130] According to this diagnostic method, although the calculation load of the synchronization process is large, the multiple first period unit data are accurately synchronized, improving the accuracy of the reference data and increasing the reliability of the state diagnosis of the object.

[0131] In one embodiment of the diagnostic method, The synchronization process may be a process of making the timing at which the amplitude of predetermined data included in the plurality of first period unit data reaches a maximum coincide with the timing at which the amplitude of each of the other data reaches a maximum.

[0132] According to this diagnostic method, the computational load of the synchronization process of the plurality of first period unit data is small. Furthermore, according to this diagnostic method, when the object repeats an operation pattern in which the amplitude of the detected physical quantity is maximized at a predetermined timing, the plurality of first period unit data is accurately synchronized, improving the accuracy of the reference data and increasing the reliability of the condition diagnosis of the object.

[0133] In one embodiment of the diagnostic method, The diagnostic step includes: extracting diagnostic object data corresponding to at least a part of the predetermined operation pattern from the second measurement data; a step of synchronizing the reference data with the diagnostic object data, and diagnosing a state of the object based on a difference between the synchronized reference data and the diagnostic object data; may include:

[0134] According to this diagnostic method, regardless of the type of object or the type of predetermined movement pattern that the object repeatedly performs, the greater the change in the object's state between the first and second periods, the greater the difference between the reference data and the diagnostic object data, making it possible to realize a highly versatile and easy-to-use diagnosis based on this difference.

[0135] In one embodiment of the diagnostic method, The diagnostic step includes: extracting a plurality of second period unit data from the second measurement data, each of which corresponds to at least a part of the predetermined movement pattern; a step of performing a synchronization process on the plurality of second period unit data and calculating a representative value of the plurality of second period unit data that have been synchronized to generate diagnostic object data; a step of synchronizing the reference data with the diagnostic object data, and diagnosing a state of the object based on a difference between the synchronized reference data and the diagnostic object data; may include:

[0136] According to this diagnostic method, second measurement data based on physical quantities generated by the object repeating a predetermined movement pattern during a second period is acquired, and multiple second period unit data extracted from the second measurement data are processed synchronously to calculate a representative value. This makes it possible to generate highly accurate diagnostic object data with reduced variation in the multiple second period unit data, thereby improving the reliability of the condition diagnosis of the object.

[0137] In one embodiment of the diagnostic method, The representative value may be an average value.

[0138] According to this diagnostic method, by synchronously processing multiple first period unit data extracted from the first measurement data and calculating the average value, it is possible to generate highly accurate reference data in which the variation and high-frequency noise of the multiple first period unit data are reduced, thereby improving the reliability of the condition diagnosis of the object.

[0139] In one embodiment of the diagnostic method, The physical quantity sensor may be an inertial sensor.

[0140] In one embodiment of the diagnostic method, the predetermined movement pattern is a pattern in which the object stops each time it performs each of a plurality of different types of movements, Each of the plurality of first period unit data may be data corresponding to any one of the plurality of actions.

[0141] According to this diagnostic method, even if there is variation in the time that the object stops moving between a first action and a second action among multiple actions, it is possible to accurately synchronize multiple first period unit data corresponding to the first action or the second action to generate highly accurate diagnostic object data, thereby improving the reliability of the object's condition diagnosis.

[0142] One aspect of the diagnostic device comprises: a first measurement data acquisition circuit that acquires first measurement data based on a time-series signal obtained by a physical quantity sensor detecting a physical quantity generated by a target object repeating a predetermined movement pattern during a first period; a reference data generating circuit that generates reference data based on the first measurement data; a second measurement data acquisition circuit that acquires second measurement data based on a time-series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeating the predetermined movement pattern during a second period; and a diagnostic circuit that diagnoses a state of the object based on the reference data and the second measurement data; Including, The reference data generating circuit A plurality of first period unit data corresponding to at least a portion of the predetermined operation pattern are extracted from the first measurement data, the plurality of first period unit data are synchronized, and a representative value of the synchronized plurality of first period unit data is calculated to generate the reference data.

[0143] According to this diagnostic device, first measurement data based on physical quantities generated by an object repeating a predetermined movement pattern in a first period is acquired, and multiple first period unit data extracted from the first measurement data are processed synchronously to calculate a representative value. This makes it possible to generate highly accurate reference data in which the variation in the multiple first period unit data is reduced, thereby improving the reliability of the condition diagnosis of the object.

[0144] One aspect of the diagnostic system includes: One aspect of the diagnostic device; the physical quantity sensor attached to the object; Equipped with.

[0145] According to this diagnostic system, the diagnostic device acquires first measurement data based on physical quantities generated when an object repeats a predetermined movement pattern during a first period, and synchronously processes multiple first period unit data extracted from the first measurement data to calculate a representative value.This makes it possible to generate highly accurate reference data in which the variation in the multiple first period unit data is reduced, thereby improving the reliability of the condition diagnosis of the object. [Explanation of symbols]

[0146] 1...object, 2...movable body, 3...casing, 10...diagnostic system, 100...diagnostic device, 110...processing circuit, 111...first measurement data acquisition circuit, 112...reference data generation circuit, 113...second measurement data acquisition circuit, 114...diagnostic circuit, 120...memory circuit, 121...diagnostic program, 130...operation unit, 140...display unit, 150...sound output unit, 160...communication unit, 200...physical quantity sensor, 210...analog front end, 220...display device

Claims

1. The physical quantity sensor detects the movement of the object by repeating a predetermined movement pattern during the first period. a first measurement to acquire first measurement data based on a time series signal obtained by detecting a physical quantity generated by the a constant data acquisition step; a reference data generating step of generating reference data based on the first measurement data; The physical quantity sensor detects whether the object repeats the predetermined movement pattern during a second period. The second measurement data is obtained based on a time series signal obtained by detecting a physical quantity generated by the a second measurement data acquisition step for obtaining the second measurement data; A diagnosis method for diagnosing a state of the object based on the reference data and the second measurement data. The process and Including, The reference data generating step includes: From the first measurement data, extracting a plurality of first period unit data; performing a synchronization process on the plurality of first period unit data; generating the reference data by calculating a representative value of the unit data; Including, The synchronization process is performed when the amplitude of predetermined data included in the plurality of first period unit data is maximum. The timing at which the amplitude of each of the other data is maximized should be matched. A diagnostic method.

2. In claim 1, The reference data generating step includes: A diagnostic method comprising the step of displaying waveforms of the plurality of first period unit data.

3. In claim 1 or 2, The diagnostic step includes: A diagnostic parameter corresponding to at least a part of the predetermined operation pattern is determined from the second measurement data. extracting the image data; The reference data and the diagnostic object data are synchronized with each other, and the synchronized reference data is diagnosing the state of the object based on a difference between the diagnostic object data and the diagnostic object data; A diagnostic method comprising:

4. In claim 1 or 2, The diagnostic step includes: From the second measurement data, extracting a plurality of second period unit data; The plurality of second period unit data are synchronized, and the synchronized plurality of second period unit data are generating diagnostic object data by calculating a representative value of the unit data; The reference data and the diagnostic object data are synchronized with each other, and the synchronized reference data is diagnosing the state of the object based on a difference between the diagnostic object data and the diagnostic object data; A diagnostic method comprising:

5. In any one of claims 1 to 4, A diagnostic method, wherein the representative value is an average value.

6. In any one of claims 1 to 5, A diagnostic method, wherein the physical quantity sensor is an inertial sensor.

7. In any one of claims 1 to 6, The predetermined movement pattern is a movement pattern in which the object moves each time the object performs each of a plurality of different movements. This is a pattern that stops the operation. Each of the plurality of first period unit data is data corresponding to any one of the plurality of actions. A diagnostic method.

8. The physical quantity sensor detects the movement of the object by repeating a predetermined movement pattern during the first period. a first measurement to acquire first measurement data based on a time series signal obtained by detecting a physical quantity generated by the a constant data acquisition circuit; a reference data generating circuit that generates reference data based on the first measurement data; The physical quantity sensor detects whether the object repeats the predetermined movement pattern during a second period. The second measurement data is obtained based on a time series signal obtained by detecting a physical quantity generated by the a second measurement data acquisition circuit for acquiring the second measurement data; A diagnosis method for diagnosing a state of the object based on the reference data and the second measurement data. The circuit and Including, The reference data generating circuit From the first measurement data, extracting a plurality of first period unit data from the plurality of first period unit data; and performing synchronization processing on the plurality of first period unit data. The reference value is calculated by calculating a representative value of the plurality of first period unit data that have been synchronized. Generate the data, The synchronization process is performed when the amplitude of predetermined data included in the plurality of first period unit data is maximum. The timing at which the amplitude of each of the other data is maximized should be matched. A diagnostic device that performs this process.

9. The diagnostic device according to claim 8 ; the physical quantity sensor attached to the object; A diagnostic system comprising:

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