Diagnostic method, diagnostic device and diagnostic system

Abstract

Provided are a diagnostic method, diagnostic device, and diagnostic system capable of improving the reliability of object condition diagnosis. The diagnostic method includes the following steps: acquiring first measurement data of a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period; a baseline data generation step for generating baseline data based on the first measurement data; acquiring second measurement data of a physical quantity generated by the object repeatedly performing the predetermined motion pattern during a second period; and diagnosing the object condition based on the baseline data and the second measurement data. The baseline data generation step includes the following steps: extracting a plurality of first-period unit data, each corresponding to at least a portion of the predetermined motion pattern, from the first measurement data; and calculating a representative value of the plurality of first-period unit data after synchronization processing to generate the baseline data.

Description

Technical Field

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

[0002] Patent document 1 describes a fault prediction device as described below, which comprises: a vibration detection unit that detects the vibration mode of each moving part generated from multiple moving parts constituting production equipment through the free end of the moving part; a storage unit that stores a reference vibration mode at the free end of the production equipment during normal operation; and a fault prediction unit that compares the vibration mode detected at any time by the vibration detection unit with the reference vibration mode stored in the storage unit to achieve fault prediction of the production equipment.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Laid-Open No. 5-52712.

[0006] 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. Summary of the Invention

[0007] One embodiment of the diagnostic method of the present invention comprises:

[0008] a first measurement data acquisition step of acquiring first measurement data based on a time series signal obtained by a physical quantity sensor detecting a physical quantity generated by an object repeatedly performing a predetermined motion pattern during a first period;

[0009] a reference data generating step of generating reference data based on the first measurement data;

[0010] 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 when the object repeatedly performs the predetermined motion pattern during a second period; and

[0011] a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data;

[0012] The benchmark data generation process includes the following steps:

[0013] 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

[0014] The plurality of first-period unit data are synchronized, and representative values ​​of the plurality of first-period unit data after the synchronization process are calculated, thereby generating the reference data.

[0015] One embodiment of the diagnostic device according to the present invention includes:

[0016] a first measurement data acquisition circuit for acquiring first measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period;

[0017] a reference data generating circuit for generating reference data based on the first measurement data;

[0018] a second measurement data acquisition circuit for acquiring second measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated when the object repeatedly performs the predetermined motion pattern during a second period; and

[0019] a diagnostic circuit for diagnosing a state of the object based on the reference data and the second measurement data;

[0020] The reference data generating circuit,

[0021] A plurality of first-period unit data corresponding to at least a portion of the predetermined action pattern are extracted from the first measurement data, synchronization processing is performed on the plurality of first-period unit data, and representative values ​​of the plurality of first-period unit data after synchronization processing are calculated, thereby generating the benchmark data.

[0022] One embodiment of the diagnostic system according to the present invention comprises:

[0023] One embodiment of the diagnostic device; and

[0024] The physical quantity sensor is mounted on the object. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flowchart showing the procedure of the diagnostic method according to the first embodiment.

[0026] Figure 2 This is a flowchart showing an example of the procedure of the reference data generating step.

[0027] Figure 3 This is a diagram showing a portion of the first measurement data in the first embodiment.

[0028] Figure 4 This is a diagram for explaining the synchronization process in the first embodiment.

[0029] Figure 5 This is a diagram for explaining the synchronization process in the first embodiment.

[0030] Figure 6 This is a diagram for explaining the synchronization process in the first embodiment.

[0031] Figure 7 3 is a diagram showing the waveform of reference data in the first embodiment.

[0032] Figure 8 3 is a diagram showing a frequency spectrum obtained by performing fast Fourier transform on reference data.

[0033] Figure 9 This is a flowchart showing an example of the procedure of the diagnosis process.

[0034] Figure 10 This is a diagram showing an example of a Lissajous figure.

[0035] Figure 11 It is a diagram showing a configuration example of a diagnostic device.

[0036] Figure 12 This is a diagram showing a part of the first measurement data in the second embodiment.

[0037] Figure 13 It is a diagram for explaining the synchronization process in the second embodiment.

[0038] Figure 14 It is a diagram for explaining the synchronization process in the second embodiment.

[0039] Figure 15 3 is a diagram showing the waveform of reference data in the second embodiment.

[0040] Figure 16 This is a flowchart showing an example of the procedure of the diagnostic process in the diagnostic method according to the third embodiment.

[0041] Figure 17 This is a flowchart showing an example of the procedure of the reference data generating step in the fourth embodiment.

[0042] Figure 18 This 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.

[0043] Figure 19 This is a flowchart showing an example of the procedure of the diagnosis process in the fourth embodiment.

[0044] Figure 20This is a flowchart showing another example of the procedure of the diagnostic process in the diagnostic method according to the fourth embodiment.

[0045] Figure 21 This is a diagram showing an example configuration of a diagnostic system according to this embodiment.

[0046] Explanation of symbols

[0047] 1. Object; 2. Movable body; 3. Housing; 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. Storage circuit; 121. Diagnostic program; 130. Operating unit; 140. Display unit; 150. Sound output unit; 160. Communication unit; 200. Physical quantity sensor; 210. Analog front end; 220. Display device. DETAILED DESCRIPTION

[0048] Below, preferred embodiments of the present invention are described in detail using the accompanying drawings. It should be noted that the embodiments described below are not intended to unduly limit the content of the present invention as described in the claims. In addition, not all of the structures described below are essential components of the present invention.

[0049] 1. Diagnostic method and diagnostic device

[0050] 1-1. First embodiment

[0051] 1-1-1. Diagnostic Methods

[0052] Figure 1 FIG. 1 is a flowchart showing the procedure of the diagnostic method of the first embodiment. Figure 1 As shown, the diagnostic method of 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 of the first embodiment is performed, for example, by a diagnostic device 100. An example configuration of the diagnostic device 100 for performing the diagnostic method of the first embodiment will be described later.

[0053] like Figure 1 As shown, first, in a first measurement data acquisition step S1 , the diagnostic apparatus 100 acquires first measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated when the object repeatedly performs a predetermined motion pattern during a first period.

[0054] The first period may be, for example, a predetermined period during which the object operates normally, such as immediately after the object is installed.

[0055] The object is an object to be diagnosed, and its type is not particularly limited. For example, it may be various devices such as electric motors and motors having a rotating mechanism or a vibrating mechanism, or an electric circuit that generates a periodic signal.

[0056] The predetermined motion pattern repeatedly performed by the object during the first period may be a pattern in which the object stops after performing a single motion, or a pattern in which the object stops each time it performs a plurality of different types of motions. For example, if the object is a motor, the object may repeatedly rotate clockwise and stop rotating, or it may repeatedly rotate clockwise, stop rotating, rotate counterclockwise, and stop rotating.

[0057] The type of physical quantity generated by the object repeatedly performing a predetermined motion pattern is not particularly limited. For example, the physical quantity may be acceleration, angular velocity, speed, displacement, pressure, current, voltage, or the like.

[0058] For example, a physical quantity sensor may also be an inertial sensor. An inertial sensor may be, for example, an acceleration sensor, a velocity sensor, an angular velocity sensor, or an IMU having a variety of sensors. IMU is the abbreviation of Inertial Measurement Unit. A physical quantity sensor may also be, for example, a sensor using a MEMS oscillator or a sensor using a crystal oscillator. MEMS is the abbreviation of Micro Electro Mechanical Systems. The detection axis of a physical quantity sensor may be one or more.

[0059] The first measurement data may be time-series data of a digital signal output from the physical quantity sensor, or may be time-series data of a digital signal obtained by converting an analog signal output from the physical quantity sensor through an analog front end.

[0060] Next, in the reference data generating step S2 , the diagnostic apparatus 100 generates reference data based on the first measurement data acquired in the step S1 .

[0061] The diagnostic apparatus 100 then waits until the set time has elapsed in step S3. If the set time has elapsed, in a second measurement data acquisition step S4, the diagnostic apparatus 100 acquires second measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeatedly performing a predetermined motion pattern during the second period.

[0062] In this embodiment, the second period is a period after the first period, for example, a predetermined period such as a few days, a few months, or a few years after the first period. The predetermined motion pattern repeatedly performed by the object during the second period is the same as the predetermined motion pattern repeatedly performed by the object during the first period.

[0063] The second measurement data may be time-series data of a digital signal output from the physical quantity sensor, or may be time-series data of a digital signal obtained by converting an analog signal output from the physical quantity sensor through an analog front end.

[0064] Next, in the diagnosis step S5 , the diagnosis apparatus 100 diagnoses the state of the object based on the reference data generated in the step S2 and the second measurement data acquired in the step S4 .

[0065] The diagnostic apparatus 100 may also be configured to determine whether the object is normal or abnormal during the second period, assuming the object is normal during the first period. Furthermore, the diagnostic apparatus 100 may also diagnose the extent of changes in the object based on the passage of time from the first period to the second period.

[0066] The diagnostic device 100 repeats steps S3 to S5 until the diagnosis is completed (N in step S6). Note that the waiting time in step S3 may be a fixed value or a variable value that is appropriately set each time.

[0067] Figure 2 It shows Figure 1 This is a flowchart of an example of the procedure of the reference data generation step S2. Figure 2 As shown, first, in step S21, the diagnostic device 100 Figure 1 From the first measurement data acquired in step S1, a plurality of first-period unit data items corresponding to at least a portion of a predetermined motion pattern repeatedly performed by the object during the first period are extracted. For example, if the predetermined motion pattern is one in which the object stops performing a certain motion, each of the plurality of first-period unit data items may also be data corresponding to that motion. Furthermore, if the predetermined motion pattern is one in which the object stops performing each of a plurality of different motions, each of the plurality of first-period unit data items may also be data corresponding to the plurality of motions.

[0068] Then, in step S22 , the diagnostic apparatus 100 displays the plurality of first-period unit data extracted in step S21 on a display unit (not shown).

[0069] Finally, in step S23, the diagnostic device 100 synchronizes the plurality of first-period unit data extracted in step S21, calculates a representative value for the synchronized plurality of first-period unit data, and generates baseline data. Synchronization minimizes the difference between predetermined data included in the plurality of first-period unit data and other data, thereby aligning timing. Examples of representative values ​​include an average value or a median value.

[0070] Figure 3 1 is a diagram showing a portion of the first measurement data acquired in the first measurement data acquisition step S1. In the case where the motor as the object repeatedly performs a predetermined operation pattern consisting of a first action, a stop, a second action, and a stop during the first period, Figure 3 The first measurement data shown is based on a portion of speed data of a time series signal output from a speed sensor as a physical quantity sensor. Figure 3 As shown, in step S21 , the diagnostic apparatus 100 extracts data when the motor performs the first operation, stop, and second operation as part of a predetermined operation pattern for the nth time from the first measurement data as nth first period unit data.

[0071] Figure 4 、 Figure 5 and Figure 6 It is used to explain that in step S23, Figure 3 The first first-period unit data is set as predetermined data, and the first first-period unit data and the second first-period unit data are synchronously processed. Figure 4 This is a graph in which the second first period unit data is made consistent with the first first period unit data by shifting the sample by j. The range of j is set to -j max ≦j≦j max .exist Figure 4 In the example, when the i-th sample of the first period unit data is set to A i , set the i-th sample of the second first period unit data to B i When 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 after the sample is shifted by j is calculated by formula (1): j In formula (1), when the number of samples of the first first period unit data and the number of samples of the second first period unit data are set to M, m s ≧j max 、m f ≧Mj max .

[0072] [Formula 1]

[0073]

[0074] Figure 5 Is shown about -j max ≦j≦j max For each integer j, the difference Δ calculated by formula (1) j The sequence of . Figure 5 In the example, j max = 100. The synchronization process of the first first period unit data and the second first period unit data is to set the sample offset difference Δ j The diagnostic apparatus 100 sets the range of j to -j for each of the Nth first period unit data after the second. max ≦j≦j max , calculate the difference Δ from the first period unit data by formula (1) j The sequence is then offset by the sample difference Δ j A synchronization process is performed to make the Nth first-period unit data coincide with the first first-period unit data by obtaining the smallest integer j. Figure 6 This is a diagram in which waveforms of a plurality of first-period unit data extracted from the first measurement data and synchronized are superimposed.

[0075] Figure 7 1 is a diagram showing a waveform of reference data generated by calculating an average value as a representative value of a plurality of first-period unit data after synchronization processing in step S23 . Figure 7 The waveform of the reference data is Figure 6 The waveforms of the plurality of first period unit data are averaged to obtain an averaged waveform. By averaging the waveforms of the plurality of first period unit data, high frequency noise is reduced.

[0076] Figure 8 It shows the Figure 7 Figure 2 shows the spectrum obtained by performing a high-speed Fourier transform on the reference data. Figure 8 In the frequency spectrum shown, high-frequency noise is reduced to generate a clear peak at a predetermined frequency. The reference data obtained by averaging a plurality of first-period unit data can be said to be data that accurately shows the state of the object in the first period.

[0077] Figure 9 It shows Figure 1 The following is a flowchart of an example of the sequence of the diagnostic step S5. Figure 9 As shown, first, in step S51, the diagnostic device 100 Figure 1Extracting the diagnostic target data corresponding to at least a portion of the predetermined motion pattern repeatedly performed by the object during the second period from the second measurement data acquired in step S4. Figure 2 The process of extracting arbitrary first-period unit data from the first measurement data in step S21 is similar. For example, if, similar to the first period, the object repeatedly performs a predetermined motion pattern consisting of a first motion, a stop, a second motion, and a stop again during the second period, the diagnostic apparatus 100 may extract, from the second measurement data, data corresponding to the object performing the first motion, a stop, and a second motion as part of the predetermined motion pattern for the kth time as diagnostic target data.

[0078] In addition, in step S52, the diagnostic device 100 performs Figure 1 The reference data generated in step S2 and the diagnostic target data extracted in step S51 are synchronized, and the state of the diagnostic target object is determined based on the difference between the reference data and the diagnostic target data after synchronization. Synchronization is a process to minimize the difference between the reference data and the diagnostic target data and to make the timing consistent. Specifically, when the i-th sample of the reference data is set to C i , set the i-th sample of the diagnostic object data to D i When the difference Δ between the reference data and the diagnostic target data obtained by shifting the sample by j is j The calculation is performed by the same formula (2) as the above formula (1). In formula (2), when the range of j is set to -j max ≦j≦j max , when the number of samples of the reference data and the number of samples of the diagnosis target data are set to M, m s ≧j max 、m f ≧Mj max .

[0079] [Formula 2]

[0080]

[0081] The diagnostic apparatus 100 sets the range of j to -j max ≦j≦j max , calculate the difference Δ between the reference data and the diagnostic target data by formula (2) j The sequence is then offset by the sample difference Δ j The smallest integer j is obtained to synchronize the diagnostic data with the reference data, and the difference Δ is obtained. j The minimum value min{Δ j}. In addition, at the minimum value min{Δ j} is less than a predetermined threshold, the diagnostic apparatus 100 can diagnose 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, the diagnostic apparatus 100 can diagnose 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, that is, the state change of the object is large. When the first period is a predetermined period during which the object operates normally, the minimum value min{Δ j} is less than a predetermined threshold, the diagnostic apparatus 100 can diagnose that the object in the second period is in a normal state. j} is greater than a predetermined threshold, the diagnostic apparatus 100 can diagnose that the object in the second period is in an abnormal state.

[0082] In addition, Figure 2 In step S23, the diagnostic apparatus 100 may also calculate the minimum value min{Δ j} is set as the reference value ref{min{Δ j}}, and the difference Δ between the reference data and the diagnosis object data j The minimum value min{Δ j} divided by the reference value ref{min{Δ j}}The standard value obtained std{min{Δ j}} is compared with a predetermined threshold value to diagnose the state of the object in the second period. j} are different in size, and the standard value std{min{Δ j The size of}} is almost constant, so a constant threshold value can be used in diagnosis without being affected by the characteristics of the object or the installation location of the physical quantity sensor. It should be noted that the diagnostic device 100 can also calculate the minimum value min{Δ j} is taken as the reference value ref{min{Δ j}}.

[0083] exist Figure 1 In step S5, the diagnostic device 100 may also be connected to Figure 9 The diagnostic process shown may be performed in conjunction with or in place of other diagnostic processes. Figure 9The diagnostic processing shown. For example, the diagnostic device 100 may also calculate the RMS values ​​of the baseline data and the diagnostic object data respectively, and compare the difference between the two RMS values ​​with a predetermined threshold value to diagnose the state of the object in the second period. In addition, for example, the diagnostic device 100 may also perform high-speed Fourier transform on the baseline data and the diagnostic object data respectively and calculate two frequency spectra, and compare the difference in peak frequency and peak intensity with a predetermined threshold value to diagnose the state of the object in the second period. In addition, for example, in the case where the physical quantity sensor has multiple detection axes, the diagnostic device 100 may also generate baseline data and diagnostic object data corresponding to each detection axis, calculate the Lissajous figures of the multiple baseline data and the Lissajous figures of the multiple diagnostic object data, and diagnose the state of the object in the second period based on the difference between the two Lissajous figures. Figure 10 An example of a Lissajous figure is shown. Figure 10 An example is a Lissajous plot of X-axis data and Y-axis data.

[0084] 1-1-2. Diagnostic device

[0085] Figure 11 FIG. 1 is a diagram showing a configuration example of a diagnostic apparatus 100 for executing the diagnostic method of the first embodiment. Figure 11 As shown, the diagnostic device 100 includes a physical quantity sensor 200, an analog front end 210, a processing circuit 110, a storage circuit 120, an operation unit 130, a display unit 140, a sound output unit 150, and a communication unit 160. It should be noted that the diagnostic device 100 may also be omitted or modified. Figure 11 For example, the physical quantity sensor 200 and the analog front end 210 may not be components of the diagnostic device 100 .

[0086] The physical quantity sensor 200 detects a physical quantity generated by the object repeatedly performing a predetermined motion pattern during the first and second periods, and outputs a signal corresponding to the detected physical quantity. The output signal of the physical quantity sensor 200 is input to the analog front end 210 .

[0087] 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.

[0088] The processing circuit 110 acquires the digital time-series signal output from the analog front end 210 during the first period as first measurement data, and acquires the digital time-series signal output from the analog front end 210 during the second period as second measurement data, and performs signal processing. Specifically, the processing circuit 110 executes the diagnostic program 121 stored in the storage circuit 120 and performs various computations on the first and second measurement data. In addition, the processing circuit 110 performs various processing corresponding to operation signals from the operation unit 130, transmits display signals for causing the display unit 140 to display various information, transmits audio signals for causing the audio output unit 150 to generate various sounds, and controls the communication unit 160 for data communication with an external device (not shown). The processing circuit 110 is implemented, for example, by a CPU or a DSP. CPU stands for Central Processing Unit, and DSP stands for Digital Signal Processor.

[0089] 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. Specifically, 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.

[0090] The first measurement data acquisition circuit 111 acquires first measurement data based on a time series signal, which is obtained by the physical quantity sensor 200 detecting a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period. In other words, the first measurement data acquisition circuit 111 acquires the digital time series signal output from the analog front end 210 during the first period as the first measurement data. In other words, the first measurement data acquisition circuit 111 performs Figure 1 The first measurement data acquiring circuit 111 stores the first measurement data acquired in the first measurement data acquiring circuit 111 in the storage circuit 120 .

[0091] 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 a plurality of first period unit data corresponding to at least a portion of the predetermined action pattern repeatedly performed by the object in the first period from the first measurement data, performs synchronization processing on the extracted plurality of first period unit data, calculates representative values ​​of the plurality of first period unit data after synchronization processing, and thereby generates reference data. Synchronization processing is a process for minimizing the difference between the predetermined data included in the plurality of first period unit data and each of the other data so as to make the timing consistent. For example, the reference data generation circuit 112 calculates the difference Δ between the predetermined data and each of the other data using the aforementioned formula (1): j The sequence is then offset by the sample difference Δ j The reference data generating circuit 112 may also display the extracted plurality of first period unit data on the display unit 140. That is, the reference data generating circuit 112 executes Figure 1 The reference data generation step S2 is specifically executed Figure 2 The reference data generated by the reference data generating circuit 112 is stored in the storage circuit 120 .

[0092] 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 repeatedly performing a predetermined motion pattern during the second period. That is, the second measurement data acquisition circuit 113 acquires the digital time series signal output from the analog front end 210 during the second period as the second measurement data. That is, the second measurement data acquisition circuit 113 performs Figure 1 The second measurement data acquiring circuit 113 stores the second measurement data acquired in the second measurement data acquiring circuit 113 in the storage circuit 120 .

[0093] The diagnostic circuit 114 diagnoses the state 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 also assume that the object is in a normal state during the first period and diagnose whether the object is in a normal state or an abnormal state during the second period. In addition, the diagnostic device 100 may also diagnose the extent of changes that have occurred in the object based on the passage of time from the first period to the second period. For example, the diagnostic circuit 114 may extract diagnostic object data corresponding to at least a portion of a predetermined action pattern repeatedly performed by the object during the second period from the second measurement data, perform synchronization processing on the reference data and the diagnostic object data, and diagnose the state of the object based on the difference between the reference data and the diagnostic object data after synchronization processing. Synchronization processing is processing that aligns the timing in a manner that minimizes the difference between the reference data and the diagnostic object data.

[0094] For example, the diagnostic circuit 114 sets the range of j to -j max ≦j≦j max , calculate the difference Δ between the reference data and the diagnostic target data by formula (2) j The sequence is then offset by the sample difference Δ j The smallest integer j is obtained to synchronize the diagnostic data with the reference data, and the difference Δ is obtained. j The minimum value min{Δ j}. In addition, at the minimum value min{Δ j} is less than a predetermined threshold, the diagnostic circuit 114 can diagnose 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, the diagnostic circuit 114 can diagnose 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, that is, the state change of the object is large. When the first period is a predetermined period during which the object operates normally, the minimum value min{Δ j} is less than a predetermined threshold, the diagnostic circuit 114 can diagnose that the object in the second period is in a normal state. j} is greater than a predetermined threshold, the diagnostic circuit 114 can diagnose that the object in the second period is in an abnormal state. That is, the diagnostic circuit 114 performs Figure 1 The diagnostic step S5 is to perform Figure 9 The information of the diagnosis result of the diagnosis circuit 114 is stored in the storage circuit 120.

[0095] The diagnostic circuit 114 may also perform other diagnostic processing. For example, the diagnostic circuit 114 may calculate the RMS values ​​of the baseline data and the diagnostic object data, respectively, and compare the difference between the two RMS values ​​with a predetermined threshold value to diagnose the state of the object during the second period. Furthermore, for example, the diagnostic circuit 114 may perform a high-speed Fourier transform on the baseline data and the diagnostic object data, respectively, and calculate two frequency spectra, and compare the difference in peak frequency and peak intensity with a predetermined threshold value to diagnose the state of the object during the second period. Furthermore, for example, when the physical quantity sensor 200 has multiple detection axes, the diagnostic circuit 114 may generate baseline data and diagnostic object data for each detection axis, calculate Lissajous figures of the multiple baseline data and Lissajous figures of the multiple diagnostic object data, and diagnose the state of the object during the second period based on the difference between the two Lissajous figures.

[0096] The storage circuit 120 includes ROM and RAM (not shown). ROM stands for Read Only Memory, and RAM stands for Random Access Memory. ROM stores various programs such as the diagnostic program 121 and predetermined data, while RAM stores data generated by the processing circuit 110. The RAM also serves as a work area for the processing circuit 110, storing programs and data read from the ROM, data input from the operation unit 130, and data temporarily generated by the processing circuit 110.

[0097] The operation unit 130 is an input device composed of operation keys, push button switches, and the like, and outputs an operation signal corresponding to a user's operation to the processing circuit 110 .

[0098] The display unit 140 is a display device composed of an LCD or the like, and displays various information based on the display signal output from the processing circuit 110. LCD stands for Liquid Crystal Display. A touch panel functioning as the operation unit 130 may also be provided on the display unit 140. For example, the display unit 140 may display a screen including the first measurement data, a plurality of first period unit data, reference data, second measurement data, diagnostic target data, information on the diagnostic result, and at least a portion of a Lissajous figure based on the display signal output from the processing circuit 110.

[0099] The sound output unit 150 is composed of 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 and end of the diagnostic process based on the sound signal output from the processing circuit 110.

[0100] Communication unit 160 performs various controls to establish data communication between processing circuit 110 and an external device. For example, communication unit 160 may transmit information including the first measurement data, a plurality of first period unit data, reference data, second measurement data, diagnostic target data, diagnostic result information, and at least a portion of a Lissajous figure to the external device, and the external device may display at least a portion of the received information on a display unit (not shown).

[0101] It should be noted that at least a portion of 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 can also be implemented by dedicated hardware. In addition, the diagnostic device 100 can be a single device or composed of multiple devices. For example, the physical quantity sensor 200 and the analog front end 210 can be included in the first device, and the processing circuit 110, the storage circuit 120, the operating unit 130, the display unit 140, the sound output unit 150, and the communication unit 160 can be included in a second device that is separate from the first device. In addition, for example, the processing circuit 110 and the storage circuit 120 can be implemented by a device such as a cloud server, which generates multiple first period unit data, reference data, diagnostic object data, diagnostic result information, and Lissajous figures, and transmits the generated information to a terminal including the operating unit 130, the display unit 140, the sound output unit 150, and the communication unit 160 via a communication line.

[0102] 1-1-3. Effects

[0103] In the diagnostic method of the first embodiment described above, the diagnostic device 100 acquires first measurement data of a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period, synchronizes multiple first-period unit data extracted from the first measurement data, and calculates a representative value to generate baseline data. Therefore, according to the diagnostic method of the first embodiment, the diagnostic device 100 diagnoses the object's condition based on high-precision baseline data that reduces variation among the multiple first-period unit data, thereby improving diagnostic reliability. In particular, by calculating the average value of the synchronized multiple first-period unit data, the diagnostic device 100 generates high-precision baseline data that reduces variation and high-frequency noise among the multiple first-period unit data, further improving diagnostic reliability.

[0104] Furthermore, in the diagnostic method of the first embodiment, the synchronization processing of the plurality of first-period unit data is performed to minimize the difference between the predetermined data included in the plurality of first-period unit data and the other data, thereby aligning the timing. Therefore, although the computational load of the synchronization processing is high, the plurality of first-period unit data are accurately synchronized. Therefore, the diagnostic method of the first embodiment improves the accuracy of the reference data, thereby enhancing the reliability of the object condition diagnosis.

[0105] Furthermore, in the diagnostic method of the first embodiment, the diagnostic apparatus 100 acquires second measurement data based on a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a second period, synchronizes reference data with diagnostic target data extracted from the second measurement data, and diagnoses the object's condition based on the difference between the synchronized reference data and the diagnostic target data. Therefore, according to this diagnostic method, regardless of the type of object or the type of predetermined motion pattern the object repeatedly performs, the greater the change in the object's condition between the first and second periods, the greater the difference between the reference data and the diagnostic target data. This allows for highly versatile and simple diagnosis based on this difference.

[0106] 1-2. Second embodiment

[0107] Hereinafter, regarding the second embodiment, the same components as those of the first embodiment are denoted by the same reference numerals, and the description overlapping with the first embodiment is omitted or simplified, and the description will focus on the differences from the first embodiment.

[0108] A flowchart showing the procedure of the diagnostic method of the second embodiment is shown in FIG. Figure 1 The diagrams are omitted. In the diagnostic method of the second embodiment, the processes of the first measurement data acquisition step S1 and the second measurement data acquisition step S4 are the same as those of the first embodiment. The diagnostic method of the second embodiment differs from the first embodiment in the synchronization process in step S23 of the reference data generation step S2 and step S52 of the diagnostic step S5.

[0109] In the diagnostic method of the second embodiment, the synchronization process for the plurality of first-period unit data in step S23 of the reference data generation step S2 is performed to align the timing at which the amplitude of predetermined data included in the plurality of first-period unit data reaches its maximum with the timing at which the amplitude of each of the other data reaches its maximum. For example, the diagnostic apparatus 100 performs the synchronization process as follows: the samples are offset by an integer j so that the timing at which the amplitudes of the two data reach their maximum coincides, thereby aligning each of the other data with the predetermined data.

[0110] Figure 121 is a diagram showing a portion of the first measurement data acquired in the first measurement data acquisition step S1. In the case where the motor as the object repeatedly performs a predetermined operation pattern consisting of a first action, a stop, a second action, and a stop during the first period, Figure 12 The first measurement data shown is based on a portion of speed data of a time series signal output from a speed sensor as a physical quantity sensor. Figure 12 As shown, in step S21 of the reference data generating step S2 , the diagnostic apparatus 100 extracts data when the motor performs the first operation, stop, and second operation as part of a predetermined operation pattern for the nth time from the first measurement data as the nth first period unit data.

[0111] Figure 13 and Figure 14 This is used to explain that in step S23 of the reference data generation step S2, Figure 12 FIG. 1 is a diagram showing a synchronization process of the first first-period unit data and the second first-period unit data, with the first first-period unit data being predetermined data. Figure 13 This is a graph in which the second first period unit data is made consistent with the first first period unit data by shifting the sample by j. Figure 13 In the example, when the i-th sample of the first period unit data is set to A i , set the i-th sample of the second first period unit data to B i When sample A i With sample B i+j At the same timing. At this time, determining the integer j so that the timing when the amplitude of the first first period unit data becomes maximum is consistent with the timing when the amplitude of the second first period unit data becomes maximum is equivalent to the processing of synchronizing the first first period unit data with the second first period unit data. The timing when the amplitude becomes 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 the timing of the minimum value is the timing of the maximum value when the maximum value of the first period unit data is less than the absolute value of the minimum value. Figure 13 In the example, the timing at which the amplitude of the first first-period unit data becomes maximum is the timing at which the value is minimum. Similarly, the timing at which the amplitude of the second first-period unit data becomes maximum is the timing at which the value is minimum.

[0112] For the second and subsequent N-th first period unit data, the diagnostic apparatus 100 performs synchronization processing such that the integer j is determined so that the timing at which the amplitude of the first first period unit data reaches its maximum coincides with the timing at which the amplitude of the N-th first period unit data reaches its maximum. Figure 14 This is a diagram in which waveforms of a plurality of first-period unit data extracted from the first measurement data and synchronized are superimposed.

[0113] Figure 15 This is a diagram showing a 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 generating step S2 . Figure 15 The waveform of the reference data is Figure 14 The waveform of the plurality of first period unit data is averaged to obtain an averaged waveform. By averaging the waveforms of the plurality of first period unit data, high frequency noise is reduced.

[0114] Similarly, the synchronization process between the reference data and the target data in step S52 of diagnostic step S5 is performed to align the timing at which the reference data reaches its maximum amplitude with the timing at which the target data reaches its maximum amplitude. For example, the diagnostic apparatus 100 performs the synchronization process as follows: the target data is aligned with the reference data by shifting the samples by an integer j so that the timing at which the amplitudes of the two data reach their maximum amplitudes coincides.

[0115] For example, the difference Δ between the reference data and the diagnostic target data after synchronization processing can be obtained by the above-mentioned formula (2). j , in the difference Δ j When the difference Δ is less than a predetermined threshold, the diagnostic apparatus 100 can diagnose 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 a predetermined threshold, the diagnostic apparatus 100 can determine 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, that is, the state change of the object is large. j When the difference Δ is less than a predetermined threshold, the diagnostic apparatus 100 can diagnose that the object in the second period is in a normal state. j When the value is greater than a predetermined threshold, the diagnostic apparatus 100 can diagnose that the object in the second period is in an abnormal state.

[0116] In addition, Figure 2 In step S23, the diagnostic apparatus 100 may also calculate the minimum value min{Δ j} is set as the reference value ref{min{Δ j}}, the difference Δ between the reference data and the diagnosis target data j The minimum value min{Δ j} divided by the reference value ref{min{Δ j}}The standard value std{min{Δ j}} is compared with a predetermined threshold value to diagnose the state of the object in the second period. j} is different in size, the standard value std{min{Δ j The size of}} also hardly changes, so a constant threshold value can be used in diagnosis without being affected by the characteristics of the object or the installation location of the physical quantity sensor. It should be noted that the diagnostic device 100 can also calculate the minimum value min{Δ j} is taken as the reference value ref{min{Δ j}}.

[0117] The configuration example of the diagnostic device 100 for executing the diagnostic method of the second embodiment is the same as Figure 11 However, in the second embodiment, the processing performed by the reference data generating circuit 112 and the diagnostic circuit 114 is different from that in the first embodiment.

[0118] 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 a plurality of first period unit data corresponding to at least a portion of the predetermined action pattern repeatedly performed by the object in the first period from the first measurement data, performs synchronization processing on the extracted plurality of first period unit data, calculates representative values ​​of the plurality of first period unit data after synchronization processing, and thereby generates reference data. Synchronization processing is processing that makes the timing at which the amplitude of the predetermined data included in the plurality of first period unit data becomes maximum coincide with the timing at which the amplitude of each data of the other data becomes maximum. For example, in order for the reference data generation circuit 112 to perform synchronization processing so that the timing at which the amplitudes of the two become maximum coincides, the sample is offset by an integer j so that each data of the other data coincides with the predetermined data. The representative value is, for example, an average value, a central value, etc. The reference data generation circuit 112 may also display the extracted plurality of first period unit data on the display unit 140. That is, the reference data generation circuit 112 performs Figure 1 The reference data generation step S2 is specifically executed Figure 2 The reference data generated by the reference data generating circuit 112 is stored in the storage circuit 120 .

[0119] 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 also assume that the object is in a normal state during the first period and diagnose whether the object is in a normal state or an abnormal state during the second period. Furthermore, the diagnostic device 100 may diagnose the extent of changes in the object based on the passage of 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 repeatedly performed 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. Synchronization is a process that aligns the timing at which the amplitude of the reference data reaches its maximum with the timing at which the amplitude of the diagnostic object data reaches its maximum. For example, the diagnostic circuit 114 performs synchronization by shifting the samples by an integer j so that the timing at which the amplitudes of the two data reach their maximum coincides.

[0120] For example, the difference Δ between the reference data and the diagnostic target data after synchronization processing is obtained by the above-mentioned formula (2): j , in the difference Δ j When the difference Δ is less than a predetermined threshold, the diagnostic circuit 114 can diagnose 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 a predetermined threshold, the diagnostic circuit 114 can diagnose 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, that is, the state change of the object is large. The first period is a predetermined period during which the object operates normally. j If the difference Δ is less than a predetermined threshold, the diagnostic circuit 114 can diagnose that the object in the second period is in a normal state. j When the value is greater than a predetermined threshold, the diagnostic circuit 114 can diagnose that the object in the second period is in an abnormal state. Figure 1 The diagnostic step S5 is to perform Figure 9 The information of the diagnosis result of the diagnosis circuit 114 is stored in the storage circuit 120.

[0121] The rest of the configuration of the diagnostic apparatus 100 in the second embodiment is the same as that in the first embodiment, and therefore, description thereof will be omitted.

[0122] As described above, according to the diagnostic method of the second embodiment, the synchronization processing of multiple first-period unit data is performed to align the timing at which the amplitude of predetermined data included in the multiple first-period unit data reaches its maximum with the timing at which the amplitude of each of the other data reaches its maximum. Therefore, the computational load of the synchronization processing is reduced. Furthermore, according to the diagnostic method of the second embodiment, when the object repeatedly performs an operation mode in which the amplitude of the detected physical quantity reaches its maximum at predetermined timing, the multiple first-period unit data are accurately synchronized. This improves the accuracy of the reference data and enhances the reliability of the object condition diagnosis.

[0123] Except for this, the diagnostic method of the second embodiment can achieve the same effects as those of the diagnostic method of the first embodiment.

[0124] 1-3. Third embodiment

[0125] Hereinafter, regarding the third embodiment, the same reference numerals are given to the same components as those in the first or second embodiment, and the description repeated with the first or second embodiment is omitted or simplified, and the description will focus on the differences from the first and second embodiments.

[0126] A flowchart showing the procedure of the diagnostic method of the third embodiment is shown in FIG. Figure 1 The first measurement data acquisition step S1 and the second measurement data acquisition step S4 in the diagnostic method of the third embodiment are the same as those in the first or second embodiment. The diagnostic method of the third embodiment differs from the first and second embodiments in the diagnostic step S5.

[0127] Figure 16 FIG. 1 is a flowchart showing an example of the sequence of the diagnostic step S5 in the diagnostic method of the third embodiment. Figure 16 As shown, first, in step S53, the diagnostic apparatus 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 portion of a predetermined motion pattern repeatedly performed by the object during the second period. For example, if the predetermined motion pattern is one in which the object stops performing a certain motion after performing it, each of the plurality of second-period unit data may also be data corresponding to that motion. Furthermore, if the predetermined motion pattern is one in which the object stops performing each of a plurality of different motions, each of the plurality of second-period unit data may also be data corresponding to the plurality of motions.

[0128] The processing of step S53 is the same as that in Figure 2The processing of replacing the first measurement data with the second measurement data and replacing the plurality of first period unit data with the plurality of second period unit data in step S21 is the same.

[0129] Then, in step S54, the diagnostic apparatus 100 performs synchronization processing on the plurality of second period unit data extracted in step S53, calculates representative values ​​of the plurality of second period unit data after synchronization processing, and generates diagnostic target data. As in the first embodiment, the synchronization processing may also be processing to minimize the difference between the predetermined data included in the plurality of second period unit data and each of the other data so as to align the timing. Specifically, when the i-th sample of the predetermined data is set to E i , set the i-th sample of any other data to F i When the predetermined data and the other data with the sample offset j are j The calculation is performed by the same formula (3) as the above formula (1). In formula (3), when the range of j is set to -j max ≦j≦j max , when the number of samples of the predetermined data and the number of samples of other data are set to M, m s ≧j max 、m f ≧Mj max .

[0130] [Formula 3]

[0131]

[0132] Alternatively, similar to the second embodiment, the synchronization process may be a process for aligning the timing at which the amplitude of predetermined data included in a plurality of second period unit data reaches a maximum with the timing at which the amplitude of each of the other data reaches a maximum.

[0133] The representative value is, for example, an average value, a median value, etc. For example, if the representative value is an average value, averaging the waveforms of a plurality of second period unit data generates diagnostic target data with reduced high-frequency noise.

[0134] The processing of step S54 is the same as that in Figure 2 The processing of replacing the plurality of first period unit data with the plurality of second period unit data and replacing the reference data with the diagnosis target data in step S23 is the same.

[0135] Furthermore, in step S55, the diagnostic apparatus 100 synchronizes the reference data generated in the reference data generation step S2 with the diagnostic target data generated in step S54. The condition of the object is diagnosed based on the difference between the synchronized reference data and the diagnostic target data. Similar to the first embodiment, the synchronization process may be performed to minimize the difference between the reference data and the diagnostic target data. Alternatively, similar to the second embodiment, the synchronization process may be performed to synchronize the timing at which the amplitude of the reference data reaches its maximum with the timing at which the amplitude of the diagnostic target data reaches its maximum.

[0136] The configuration example of the diagnostic device 100 for executing the diagnostic method of the third embodiment is the same as Figure 11 However, in the third embodiment, the processing performed by the diagnostic circuit 114 is different from that in the first and second embodiments.

[0137] 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, the diagnostic circuit 114 first 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 portion of a predetermined operational pattern repeatedly performed by the object during the second period. The diagnostic circuit 114 then synchronizes the extracted plurality of second-period unit data and calculates a representative value for the plurality of synchronized second-period unit data, thereby generating diagnostic target data. The synchronization process can be performed to align the timing of the predetermined data included in the plurality of second-period unit data to minimize the difference between each of the other data, or to align the timing of the maximum amplitude of the predetermined data included in the plurality of second-period unit data with the timing of the maximum amplitude of each of the other data. The representative value can be, for example, an average value or a median value. Furthermore, the diagnostic circuit 114 synchronizes the reference data with the diagnostic target data and diagnoses the condition of the object based on the difference between the synchronized reference data and the diagnostic target data. The synchronization process may be a process for aligning the timings so as to minimize the difference between the reference data and the diagnosis target data, or a process for aligning the timings so as to maximize the amplitude of the reference data and the timings so as to maximize the amplitude of the diagnosis target data. Figure 1 The diagnostic step S5 is to perform Figure 16 The information of the diagnosis result of the diagnosis circuit 114 is stored in the storage circuit 120.

[0138] The rest of the configuration of the diagnostic apparatus 100 in the third embodiment is the same as that in the first embodiment or the second embodiment, and therefore, description thereof will be omitted.

[0139] As described above, in the diagnostic method of the third embodiment, the diagnostic device 100 acquires second measurement data of a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a second period, synchronously processes multiple second-period unit data extracted from the second measurement data, and calculates a representative value, thereby generating diagnostic target data. Therefore, according to the diagnostic method of the third embodiment, the diagnostic device 100 diagnoses the condition of the object based on high-precision diagnostic target data with reduced variance among the multiple second-period unit data, thereby improving diagnostic reliability. In particular, by calculating the average value of the multiple second-period unit data after synchronous processing, the diagnostic device 100 can generate high-precision diagnostic target data with reduced variance and high-frequency noise among the multiple second-period unit data, thereby further improving diagnostic reliability.

[0140] In addition to this, the diagnostic method of the third embodiment can achieve the same effects as the diagnostic method of the first embodiment or the second embodiment.

[0141] 1-4. Fourth embodiment

[0142] Below, with respect to the fourth embodiment, the same symbols are marked on the components that are the same as any one of the first to third embodiments, the description repeated in any one of the first to third embodiments is omitted or simplified, and the contents that are different from any one of the first to third embodiments are mainly described.

[0143] In the diagnostic methods of the first to third embodiments, when a predetermined action pattern of stopping the action each time the object performs a plurality of different actions is repeatedly performed during the first period, if the stopping time varies, the accuracy of the reference data may be reduced. For example, in the aforementioned Figure 3 In the first measurement data shown, if there is a discrepancy between the pause times of the first and second actions, the timing of at least one of the sample group corresponding to the first action and the sample group corresponding to the second action in the synchronized plurality of first-period unit data will deviate, thereby reducing the accuracy of the calculated representative value. Therefore, in the diagnostic method of the fourth embodiment, the diagnostic apparatus 100 generates, synchronizes, and generates reference data for each of the plurality of actions included in the predetermined action pattern. This ensures that the accuracy of the reference data corresponding to each action is not reduced even if there is a discrepancy in the pause times.

[0144] A flowchart showing the procedure of the diagnostic method of the fourth embodiment is shown in FIG. Figure 1The fourth embodiment of the diagnostic method is identical to the first to third embodiments, and therefore, its illustration is omitted. 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 diagnostic step S5 are different from those of the first to third embodiments.

[0145] Figure 17 This is a flowchart showing an example of the procedure of the reference data generation step S2 in the fourth embodiment. Figure 17 In the example, the predetermined motion pattern repeatedly performed by the object during the first period is a pattern in which the object stops the motion each time each of the first to Nth motions of different types is performed. N is an integer greater than or equal to 2.

[0146] like Figure 17 As shown, first, in step S201, the diagnostic apparatus 100 sets the integer i to 1. In step S202, the diagnostic apparatus 100 extracts a plurality of first period unit data corresponding to the i-th action included in the predetermined action pattern from the first measurement data acquired in the first measurement data acquisition step S1.

[0147] Then, in step S203 , the diagnostic apparatus 100 displays a plurality of first-period unit data corresponding to the i-th action extracted in step S202 on a display unit (not shown).

[0148] Next, in step S204, the diagnostic device 100 synchronizes the plurality of first-period unit data corresponding to the i-th action extracted in step S202, calculates a representative value of the plurality of first-period unit data after synchronization, and thereby generates reference data corresponding to the i-th action. Synchronization can be performed to minimize the difference between predetermined data included in the plurality of first-period unit data and each of the other data, thereby aligning the timing, or to synchronize the timing at which the amplitude of the predetermined data included in the plurality of first-period unit data reaches its maximum with the timing at which the amplitude of each of the other data reaches its maximum. The representative value can be, for example, an average value or a median value.

[0149] If the integer i is not N in step S205 , the diagnostic apparatus 100 increments the integer i by 1 in step S206 and performs step S202 and subsequent steps again. If the integer i is N in step S205 , the diagnostic apparatus 100 ends the processing.

[0150] Figure 18 1 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. Figure 18The first measurement data shown is a portion of speed data based on a time series signal output from a speed sensor as a physical quantity sensor when the motor as an object repeatedly performs a predetermined operation pattern consisting of a first operation, a stop, a second operation, and a stop during a first period. Figure 18 As shown, in step S202, the diagnostic device 100 extracts data from the first measurement data when the motor performs the first motion for the nth time as the nth first-period unit data corresponding to the first motion. In step S204, the diagnostic device 100 generates reference data corresponding to the first motion. Furthermore, in step S202, the diagnostic device 100 extracts data from the first measurement data when the motor performs the second motion for the nth time as the nth first-period unit data corresponding to the second motion. In step S204, the diagnostic device 100 generates reference data corresponding to the second motion.

[0151] Figure 19 FIG. 4 is a flowchart showing an example of the procedure of the diagnosis step S5 in the fourth embodiment. Figure 19 As shown, first, the diagnostic apparatus 100 sets the integer i to 1 in step S501 , and in step S502 , the diagnostic apparatus 100 extracts diagnostic target data corresponding to the i-th action included in the predetermined action pattern from the second measurement data acquired in the second measurement data acquisition step S4 .

[0152] Next, in step S503, the diagnostic apparatus 100 synchronizes the reference data corresponding to the i-th action generated in the reference data generation step S2 with the diagnostic target data extracted in step S502. The condition of the object is diagnosed based on the difference between the synchronized reference data and the diagnostic target data. The synchronization process can be performed to minimize the difference between the reference data and the diagnostic target data, or to synchronize the timing of the maximum amplitude of the reference data with the timing of the maximum amplitude of the diagnostic target data.

[0153] If the integer i is not N in step S504 , the diagnostic apparatus 100 increments the integer i by 1 in step S505 and performs step S502 and subsequent steps again. If the integer i is N in step S504 , the diagnostic apparatus 100 ends the processing.

[0154] Figure 20 FIG. 4 is a flowchart showing another example of the sequence of the diagnostic step S5 in the diagnostic method of the fourth embodiment. Figure 20As shown, first, the diagnostic apparatus 100 sets the integer i to 1 in step S511. In step S512, the diagnostic apparatus 100 extracts a plurality of second period unit data corresponding to the i-th action included in the predetermined action pattern from the second measurement data acquired in the second measurement data acquisition step S4.

[0155] Next, in step S513, the diagnostic device 100 synchronizes the plurality of second-period unit data corresponding to the i-th action extracted in step S512, calculates a representative value of the plurality of synchronized second-period unit data, and thereby generates diagnostic target data corresponding to the i-th action. The synchronization process can be performed to minimize the difference between predetermined data included in the plurality of second-period unit data and each of the other data, thereby aligning the timing, or aligning the timing at which the amplitude of the predetermined data included in the plurality of second-period unit data reaches its maximum, with the timing at which the amplitude of each of the other data reaches its maximum. The representative value can be, for example, an average value or a median value.

[0156] Next, in step S514, the diagnostic apparatus 100 synchronizes the reference data corresponding to the i-th action generated in the reference data generation step S2 with the diagnostic target data corresponding to the i-th action generated in step S513. The condition of the object is diagnosed based on the difference between the synchronized reference data and the diagnostic target data. The synchronization process can be performed to minimize the difference between the reference data and the diagnostic target data, or to synchronize the timing of the maximum amplitude of the reference data with the timing of the maximum amplitude of the diagnostic target data.

[0157] If the integer i is not N in step S515 , the diagnostic apparatus 100 increments the integer i by 1 in step S516 and performs step S512 and subsequent steps again. If the integer i is N in step S515 , the diagnostic apparatus 100 ends the processing.

[0158] The configuration example of the diagnostic device 100 for executing the diagnostic method of the fourth embodiment is the same as Figure 11 However, in the fourth embodiment, the processing performed by the reference data generating circuit 112 and the diagnostic circuit 114 is different from that in the first embodiment.

[0159] For each integer i greater than 1 and less than N, the reference data generation circuit 112 generates reference data corresponding to the i-th action based on the first measurement data acquired by the first measurement data acquisition circuit 111. N is an integer greater than 2. Specifically, for each integer i greater than 1 and less than N, the reference data generation circuit 112 extracts from the first measurement data a plurality of first period unit data corresponding to the i-th action included in the predetermined action pattern repeatedly performed by the object in the first period, performs synchronization processing on the extracted plurality of first period unit data corresponding to the i-th action, calculates the representative value of the plurality of first period unit data after the synchronization processing, and thereby generates reference data corresponding to the i-th action. The synchronization processing can be a processing to minimize the difference between the predetermined data included in the plurality of first period unit data and each data of other data so as to make the timing consistent, or it can be a processing to make the timing when the amplitude of the predetermined data included in the plurality of first period unit data becomes the maximum coincide with the timing when the amplitude of each data of other data becomes the maximum. The representative value is, for example, an average value, a median value, etc. The reference data generation circuit 112 can also display the plurality of first period unit data corresponding to the extracted i-th action on the display unit 140. That is, the reference data generation circuit 112 performs Figure 1 The reference data generation step S2 is specifically executed Figure 17 The reference data corresponding to the first to N-th operations generated by the reference data generating circuit 112 are stored in the storage circuit 120 .

[0160] For each integer i between 1 and N, the diagnostic circuit 114 diagnoses the state of the object 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 also assume that the object is in a normal state during the first period and diagnose whether the object is in a normal state or an abnormal state during the second period. Furthermore, the diagnostic device 100 may also diagnose the extent of changes in the object based on the passage of time from the first period to the second period.

[0161] For example, for each integer i between 1 and N, the diagnostic circuit 114 may extract, from the second measurement data, diagnostic target data corresponding to the i-th action included in the predetermined action pattern repeatedly performed by the object during the second period. Furthermore, for example, for each integer i between 1 and N, the diagnostic circuit 114 may extract, from the second measurement data, multiple second-period unit data corresponding to the i-th action included in the predetermined action pattern, perform synchronization processing on the extracted multiple second-period unit data corresponding to the i-th action, calculate a representative value of the synchronized multiple second-period unit data, and thereby generate diagnostic target data corresponding to the i-th action. The synchronization processing may be processing to minimize the difference between the predetermined data included in the multiple second-period unit data and each other data to achieve timing alignment, or processing to achieve the maximum amplitude of the predetermined data included in the multiple second-period unit data to coincide with the maximum amplitude of each other data. The representative value may be, for example, an average value or a median value.

[0162] Furthermore, for each integer i greater than 1 and less than N, the diagnostic circuit 114 may also perform synchronization processing of the reference data corresponding to the i-th action and the diagnostic object data corresponding to the i-th action, and diagnose the state of the object based on the difference between the reference data and the diagnostic object data after the synchronization processing. The synchronization processing may be processing to minimize the difference between the reference data and the diagnostic object data so as to make the timing consistent, or processing to maximize the amplitude of the reference data and maximize the amplitude of the diagnostic object data so as to make the timing consistent. That is, the diagnostic circuit 114 performs Figure 1 The diagnostic step S5 is to perform Figure 19 Steps S501 to S505 or Figure 20 The information of the diagnosis result of the diagnosis circuit 114 is stored in the storage circuit 120 .

[0163] The rest of the configuration of the diagnostic apparatus 100 in the fourth embodiment is the same as that in the first embodiment, and therefore, description thereof will be omitted.

[0164] 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 moving each time it performs one of multiple different types of motions. Furthermore, each of the multiple first-period unit data extracted by the diagnostic apparatus 100 in step S202 of the reference data generation step S2 corresponds to any one of the multiple motions. Furthermore, for each integer i between 1 and N, the diagnostic apparatus 100 repeatedly performs step S202 to generate multiple first-period unit data corresponding to the i-th motion. The multiple first-period unit data corresponding to the i-th motion thus generated do not include samples corresponding to motions other than the i-th motion. Therefore, even if there is a discrepancy between the time at which the object stops moving before and after the i-th motion, the diagnostic apparatus 100 can accurately synchronize the multiple first-period unit data corresponding to the i-th motion in step S204, generating reference data for the i-th motion with high accuracy. Therefore, according to the diagnostic method of the fourth embodiment, the diagnostic apparatus 100 can diagnose the condition of the object based on the reference data corresponding to the i-th motion and the diagnostic target data, thereby improving diagnostic reliability.

[0165] In addition to this, the diagnostic method of the fourth embodiment can achieve the same effects as those of the diagnostic methods of the first to third embodiments.

[0166] 2. Diagnostic system

[0167] Below, regarding the diagnostic system of this embodiment, the same symbols are marked on the components that are the same as the components described in any of the above embodiments, the description repeated in any of the above embodiments is omitted or simplified, and the contents that are different from any of the above embodiments are mainly described.

[0168] Figure 21 : is a diagram showing an example of the configuration of the diagnostic system of this embodiment. Figure 21 As shown, 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 .

[0169] Object 1 includes a movable body 2 and a housing 3 that houses movable body 2. A physical quantity sensor 200 is attached to housing 3 and detects a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period and a second period, outputting a signal corresponding to the detected physical quantity. The output signal of physical quantity sensor 200 is input to an analog front end 210.

[0170] The analog front end 210 performs amplification processing, A / D conversion processing, and the like on the output signal of the physical quantity sensor 200 , and outputs a digital time-series signal.

[0171] The diagnostic device 100 acquires a digital time-series signal output from the analog front end 210 during a first period as first measurement data, and generates reference data based on the acquired first measurement data. Furthermore, the diagnostic device 100 acquires a digital time-series signal output from the analog front end 210 during a second period as second measurement data. Furthermore, the diagnostic device 100 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. For example, any of the first to fourth embodiments described above can be applied as the diagnostic device 100.

[0172] According to the diagnostic system 10 of the present embodiment, the diagnostic device 100 can improve the reliability of the state diagnosis of the object 1 .

[0173] The present invention is not limited to the present embodiment, and various modifications can be made without departing from the spirit of the present invention.

[0174] The above-mentioned embodiment and modification examples are merely examples, and the present invention is not limited to these examples. For example, the embodiments and modification examples may be appropriately combined.

[0175] The present invention includes substantially the same configuration as that described in the embodiment, for example, a configuration having the same function, method, and result, or a configuration having the same purpose and effect. In addition, the present invention includes a configuration that replaces a non-essential part of the configuration described in the embodiment. In addition, the present invention includes a configuration that can achieve the same effect as that described in the embodiment or a configuration that can achieve the same purpose. In addition, the present invention includes a configuration in which a known technology is added to the configuration described in the embodiment.

[0176] The following contents are derived from the above-mentioned embodiment and modification examples.

[0177] One approach to diagnosis includes:

[0178] a first measurement data acquisition step of acquiring first measurement data based on a time series signal obtained by a physical quantity sensor detecting a physical quantity generated by an object repeatedly performing a predetermined motion pattern during a first period;

[0179] a reference data generating step of generating reference data based on the first measurement data;

[0180] 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 when the object repeatedly performs the predetermined motion pattern during a second period; and

[0181] a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data;

[0182] The benchmark data generation process includes the following steps:

[0183] 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

[0184] The plurality of first-period unit data are synchronized, and representative values ​​of the plurality of first-period unit data after the synchronization process are calculated, thereby generating the reference data.

[0185] According to this diagnostic method, first measurement data of physical quantities generated by the object repeatedly performing a predetermined action pattern in a first period is obtained, and multiple first-period unit data extracted from the first measurement data are synchronously processed and representative values ​​are calculated, so that high-precision baseline data with reduced deviations of multiple first-period unit data can be generated, thereby improving the reliability of the state diagnosis of the object.

[0186] Alternatively, in one embodiment of the diagnostic method,

[0187] The reference data generating step includes a step of displaying waveforms of the plurality of first-period unit data.

[0188] Alternatively, in one embodiment of the diagnostic method,

[0189] The synchronization process is a process for aligning timings so as to minimize differences between predetermined data included in the plurality of first period unit data and other data.

[0190] According to this diagnostic method, although the calculation load of the synchronization process is large, the plurality of first period unit data are accurately synchronized, so the accuracy of the reference data is improved, and the reliability of the state diagnosis of the object can be improved.

[0191] Alternatively, in one embodiment of the diagnostic method,

[0192] The synchronization process is a process of aligning the timing at which the amplitude of predetermined data included in the plurality of first period unit data reaches a maximum with the timing at which the amplitude of each of the other data reaches a maximum.

[0193] This diagnostic method reduces the computational load of synchronizing multiple first-period unit data. Furthermore, when the object repeatedly operates in an operating mode in which the amplitude of the detected physical quantity reaches a maximum at predetermined timing, the multiple first-period unit data are accurately synchronized. This improves the accuracy of the reference data and enhances the reliability of the object's condition diagnosis.

[0194] Alternatively, in one embodiment of the diagnostic method,

[0195] The diagnostic process includes the following steps:

[0196] extracting diagnostic target data corresponding to at least a portion of the predetermined action pattern from the second measurement data; and

[0197] The reference data and the diagnosis target data are synchronized, and the state of the object is diagnosed based on a difference between the reference data and the diagnosis target data after the synchronization process.

[0198] According to this diagnostic method, the type of object and the type of predetermined action pattern repeatedly performed by the object are not affected. Between the first period and the second period, the greater the state change of the object, the greater the difference between the baseline data and the diagnostic object data. Therefore, a highly universal and simple diagnosis can be achieved based on this difference.

[0199] Alternatively, in one embodiment of the diagnostic method,

[0200] The diagnostic process includes the following steps:

[0201] extracting a plurality of second period unit data respectively corresponding to at least a portion of the predetermined action pattern from the second measurement data;

[0202] performing synchronization processing on the plurality of second-period unit data, calculating representative values ​​of the plurality of second-period unit data after the synchronization processing, and thereby generating diagnosis target data; and

[0203] The reference data and the diagnosis target data are synchronized, and the state of the object is diagnosed based on a difference between the reference data and the diagnosis target data after the synchronization process.

[0204] According to this diagnostic method, second measurement data of physical quantities generated by the object repeatedly performing a predetermined action pattern in the second period is obtained, and multiple second-period unit data extracted from the second measurement data are synchronously processed and representative values ​​are calculated, thereby generating high-precision diagnostic object data with reduced deviations of multiple second-period unit data, thereby improving the reliability of the state diagnosis of the object.

[0205] Alternatively, in one embodiment of the diagnostic method,

[0206] The representative values ​​are average values.

[0207] According to this diagnostic method, multiple first-period unit data extracted from the first measurement data are synchronously processed and the average value is calculated, thereby generating high-precision baseline data with reduced deviation and high-frequency noise in the multiple first-period unit data, thereby improving the reliability of the state diagnosis of the object.

[0208] Alternatively, in one embodiment of the diagnostic method,

[0209] The physical quantity sensor is an inertial sensor.

[0210] Alternatively, in one embodiment of the diagnostic method,

[0211] The predetermined motion pattern is a pattern in which the object stops moving each time the object performs each of a plurality of different types of motions.

[0212] Each of the plurality of first period unit data is data corresponding to any one of the plurality of actions.

[0213] According to this diagnostic method, even if there is a deviation in the time when the object stops moving between the first action and the second action among multiple actions, multiple first-period unit data corresponding to the first action or the second action can be correctly synchronized and high-precision diagnostic object data can be generated, thereby improving the reliability of the state diagnosis of the object.

[0214] One approach to diagnostic devices includes:

[0215] a first measurement data acquisition circuit for acquiring first measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period;

[0216] a reference data generating circuit for generating reference data based on the first measurement data;

[0217] a second measurement data acquisition circuit for acquiring second measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated when the object repeatedly performs the predetermined motion pattern during a second period; and

[0218] a diagnostic circuit for diagnosing a state of the object based on the reference data and the second measurement data;

[0219] The reference data generating circuit,

[0220] A plurality of first-period unit data corresponding to at least a portion of the predetermined action pattern are extracted from the first measurement data, synchronization processing is performed on the plurality of first-period unit data, and representative values ​​of the plurality of first-period unit data after synchronization processing are calculated, thereby generating the benchmark data.

[0221] According to the diagnostic device, first measurement data of physical quantities generated by the object repeatedly performing a predetermined action pattern in a first period is obtained, and multiple first-period unit data extracted from the first measurement data are synchronously processed and representative values ​​are calculated, so that high-precision baseline data with reduced deviations of multiple first-period unit data can be generated, thereby improving the reliability of the state diagnosis of the object.

[0222] One approach to diagnosing a system is to have:

[0223] One embodiment of the diagnostic device; and

[0224] The physical quantity sensor is mounted on the object.

[0225] According to the diagnostic system, the diagnostic device obtains first measurement data of physical quantities generated by the object repeatedly performing a predetermined action pattern in a first period, synchronously processes multiple first-period unit data extracted from the first measurement data and calculates representative values, thereby generating high-precision baseline data with reduced deviations of multiple first-period unit data, thereby improving the reliability of the state diagnosis of the object.

Claims

1. A diagnostic method, characterized in that: include: a first measurement data acquisition step of acquiring first measurement data based on a time series signal obtained by a physical quantity sensor detecting a physical quantity generated by an object repeatedly performing a predetermined motion pattern during a first period; 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 repeatedly performing the predetermined motion pattern during a second period; as well as a diagnosis step of diagnosing a state of the object based on the reference data and the second measurement data; The benchmark data generation process includes the following steps: extracting a plurality of first period unit data respectively corresponding to at least a portion of the predetermined action pattern from the first measurement data; as well as performing synchronization processing on the plurality of first period unit data, calculating representative values ​​of the plurality of first period unit data after synchronization processing, thereby generating the reference data, The synchronization processing is a processing for minimizing the difference between a predetermined first-period unit data included in the multiple first-period unit data and each data of the other first-period unit data so as to make the timing consistent, or the synchronization processing is a processing for making the timing when the amplitude of a predetermined first-period unit data included in the multiple first-period unit data becomes the maximum and the timing when the amplitude of each data of the other first-period unit data becomes the maximum consistent.

2. The diagnostic method according to claim 1, wherein The reference data generating step includes a step of displaying waveforms of the plurality of first-period unit data.

3. The diagnostic method according to claim 1, wherein The diagnostic process includes the following steps: extracting, from the second measurement data, diagnostic target data corresponding to at least a portion of the predetermined action pattern; as well as The reference data and the diagnosis target data are synchronized, and the state of the object is diagnosed based on a difference between the reference data and the diagnosis target data after the synchronization process.

4. The diagnostic method according to claim 1, wherein The diagnostic process includes the following steps: extracting a plurality of second period unit data respectively corresponding to at least a portion of the predetermined action pattern from the second measurement data; performing synchronization processing on the plurality of second period unit data, calculating representative values ​​of the plurality of second period unit data after the synchronization processing, and thereby generating diagnosis target data; as well as The reference data and the diagnosis target data are synchronized, and the state of the object is diagnosed based on a difference between the reference data and the diagnosis target data after the synchronization process.

5. The diagnostic method according to claim 1, wherein The representative values ​​are average values.

6. The diagnostic method according to claim 1, characterized in that The physical quantity sensor is an inertial sensor.

7. The diagnostic method according to claim 1, characterized in that The predetermined motion pattern is a pattern in which the object stops moving each time the object performs each of a plurality of different types of motions. Each of the plurality of first period unit data is data corresponding to any one of the plurality of actions.

8. A diagnostic device, characterized in that include: a first measurement data acquisition circuit for acquiring first measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeatedly performing a predetermined motion pattern during a first period; a reference data generating circuit for generating reference data based on the first measurement data; a second measurement data acquisition circuit for acquiring second measurement data based on a time series signal obtained by the physical quantity sensor detecting a physical quantity generated by the object repeatedly performing the predetermined motion pattern during a second period; as well as a diagnostic circuit for diagnosing a state of the object based on the reference data and the second measurement data; The reference data generating circuit, extracting a plurality of first period unit data corresponding to at least a portion of the predetermined action pattern from the first measurement data, performing synchronization processing on the plurality of first period unit data, and calculating a representative value of the plurality of first period unit data after the synchronization processing, thereby generating the reference data; The synchronization processing is a processing for minimizing the difference between a predetermined first-period unit data included in the multiple first-period unit data and each data of the other first-period unit data so as to make the timing consistent, or the synchronization processing is a processing for making the timing when the amplitude of a predetermined first-period unit data included in the multiple first-period unit data becomes the maximum and the timing when the amplitude of each data of the other first-period unit data becomes the maximum consistent.

9. A diagnostic system, characterized in that: include: The diagnostic device according to claim 8; as well as The physical quantity sensor is mounted on the object.

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