Synchronization device and storage medium

Abstract

The synchronization device (1) comprises a machine data acquisition unit (11), a measurement data acquisition unit (12), a correlation calculation unit (13) and a synchronization data output unit (14). The machine data acquisition unit (11) acquires time series information related to the driving of the machine as machine data. The measurement data acquisition unit (12) acquires time series information related to the state of the machine as measurement data. The correlation calculation unit (13) calculates the time difference when the strength of the correlation between the machine data and the measurement data becomes maximum, i.e., the correlation time difference, based on the machine data, the measurement data and the time difference when any one of the machine data and the measurement data is shifted in the positive or negative direction of the time axis. The synchronization data output unit (14) outputs the machine data synchronized with the measurement data based on the correlation time difference as synchronized machine data, and outputs the measurement data synchronized with the machine data based on the correlation time difference as synchronized measurement data.

Description

Technical Field

[0001] The present invention relates to a synchronization device and a storage medium for synchronously outputting a plurality of time-series data. Background Art

[0002] Generally speaking, it is known that in a device having a power source such as an electric motor, the driving sound of the electric motor or the machine that is the driving object of the electric motor contains a large amount of information related to the state of the power source and the driven object. Therefore, in order to determine the state of the device, the following technology is sought, that is, obtaining machine data such as position, speed, and torque of the motor or the machine, and obtaining data related to the driving sound and vibration of the motor or the machine measured by sensors, that is, measurement data, and synchronizing these data with each other for analysis. However, the system for obtaining machine data and the system for obtaining measurement data are basically different systems, so some kind of modification is required. Against the above technical background, the following patent document 1 discloses the following technology, that is, for machine data and measurement data measured by different systems, the time when a predetermined feature is exhibited is extracted, thereby synchronizing these two data with each other.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2019-219725 Summary of the invention

[0004] In the technology of Patent Document 1, as the moment of showing a pre-set characteristic, the moment when the size of the processing sound exceeds a threshold value, or the moment when the acceleration of the vibration of the tool exceeds a threshold value is exemplified. However, the moment of showing these characteristics varies according to the driving conditions such as the structure of the machine, the type of tool used for processing, the material of the workpiece being processed, and the action mode. Therefore, in the technology of Patent Document 1, there is a problem that the moment of showing the characteristic must be reset according to the driving conditions. That is, in the technology of Patent Document 1, it is necessary to perform setting operations for preparing the measurement before the measurement, and there is a problem that the synchronous processing cannot be implemented efficiently.

[0005] The present invention has been made in view of the above-mentioned situation, and an object of the present invention is to obtain a synchronization device capable of efficiently performing synchronization processing.

[0006] In order to solve the above-mentioned problems and achieve the purpose, the synchronization device involved in the present invention has a machine data acquisition unit, a measurement data acquisition unit, a correlation calculation unit and a synchronization data output unit. The machine data acquisition unit acquires time series information related to the driving of the machine as machine data. The measurement data acquisition unit acquires time series information related to the state of the machine as measurement data. The correlation calculation unit calculates the time difference when the strength of the correlation between the machine data and the measurement data becomes the maximum, that is, the correlation time difference, based on the machine data, the measurement data and the time difference when any one of the machine data and the measurement data is shifted in the positive or negative direction of the time axis. The synchronization data output unit outputs the machine data synchronized with the measurement data based on the correlation time difference as synchronized machine data. In addition, the synchronization data output unit outputs the measurement data synchronized with the machine data based on the correlation time difference as synchronized measurement data.

[0007] Effects of the Invention

[0008] According to the synchronization device according to the present invention, there is an effect that synchronization processing can be efficiently performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a block diagram showing an example of the functional configuration of the synchronization device according to the first embodiment.

[0010] Figure 2 This is a diagram showing a configuration example of a drive system including a drive device having the function of the synchronizing device according to the first embodiment.

[0011] Figure 3 Yes means Figure 2 1 is a block diagram showing an example of a functional configuration of a drive device according to the first embodiment.

[0012] Figure 4 This is a flowchart showing an example of the processing procedure of the synchronization process in the first embodiment.

[0013] Figure 5 Yes means Figure 4 A waveform diagram illustrating an example of the machine data used.

[0014] Figure 6 Yes means Figure 4 A waveform diagram of an example of measurement data used for the description.

[0015] Figure 7 This is a block diagram showing an example of the functional configuration of the correlation calculation unit in the first embodiment.

[0016] Figure 8 Yes means Figure 4A flowchart showing an example of a processing procedure of a correlation operation shown.

[0017] Fig. 9 This is a waveform diagram used in the description of the output processing of the synchronous data in the first embodiment.

[0018] Fig.10 This is a block diagram showing an example of the functional configuration of a synchronization device according to the second embodiment.

[0019] Fig.11 This is a block diagram showing an example of the functional structure of the correlation calculation unit in the second embodiment.

[0020] Fig.12 This is a diagram showing a configuration example of a data analysis system including a data analysis device having the function of a synchronization device according to the second embodiment.

[0021] Fig.13 Yes means Fig.12 A block diagram showing an example of the functional structure of the data analysis device shown.

[0022] Fig.14 This is a block diagram showing an example of the functional configuration of a visualization terminal according to the second embodiment.

[0023] Fig.15 This is a diagram showing a display example of a visualization terminal based on analysis results of a data analysis device in the second embodiment.

[0024] Fig.16 This is a block diagram showing an example of the functional configuration of a synchronization device according to the third embodiment.

[0025] Fig.17 This is a block diagram showing an example of the functional structure of the correlation calculation unit in the third embodiment.

[0026] Fig.18 This is a diagram showing a configuration example of a simulation system including a simulation terminal having the function of a synchronization device according to the third embodiment.

[0027] Fig.19 This is a block diagram showing an example of the functional structure of the emulation terminal involved in the third embodiment. DETAILED DESCRIPTION

[0028] Hereinafter, a synchronization device and a storage medium according to embodiments of the present invention will be described in detail with reference to the drawings.

[0029] Implementation method 1.

[0030] Figure 1This is a block diagram showing an example of the functional configuration of the synchronization device 1 according to Embodiment 1. The synchronization device 1 according to Embodiment 1 includes a device data acquisition unit 11 , a measurement data acquisition unit 12 , a correlation calculation unit 13 , and a synchronization data output unit 14 .

[0031] The machine data acquisition unit 11 acquires time series information related to the operation of a machine (not shown) connected to the synchronization device 1 as machine data. The machine data is managed based on the first time information. The first time information is information indicating the time of acquisition of the machine data. The machine data is information related to the driving of a machine driven by a power source such as an electric motor. Examples of machine data include command values ​​related to the rotation angle, position, rotation speed, current, and thrust of the electric motor connected to the machine, or their measured values. The machine data acquisition unit 11 acquires at least one type of machine data. For example, machine data related to the rotation angle and machine data related to the position are different types of machine data, and machine data of different time periods related to the rotation angle are the same type of machine data. When acquiring multiple types of machine data, the multiple types of machine data correspond to each other through the first time information, and each is acquired at a predetermined sampling period.

[0032] The measurement data acquisition unit 12 acquires time series information related to the state of the machine connected to the synchronization device 1 as measurement data. The measurement data is managed based on the second time information. The second time information is information indicating the time of acquisition of the measurement data, and is acquired together with the measurement data. The measurement data is information obtained by measuring the state of the machine driven by a power source such as a motor by the machine or a sensor installed on the machine. Examples of measurement data include the driving sound of the machine, the position of the movable part of the machine, the acceleration of the vibration in the movable part of the machine, the force and pressure received from the movable part of the machine, and the video obtained by shooting the driving state of the machine. In addition, the measurement data may not directly use the measurement value measured by the sensor, but use a calculated value calculated by more than or equal to one measurement value. The measurement data acquisition unit 12 acquires at least one type of measurement data. For example, the measurement data related to the driving sound of the machine and the measurement data related to the position of the movable part of the machine are different types of measurement data, and the measurement data of different time periods related to the driving sound of the machine are the same type of measurement data. The measurement data is acquired at a predetermined sampling period. The sampling period of the measurement data may be different from the sampling period of the device data.

[0033] The correlation calculation unit 13 calculates the correlation evaluation value based on the machine data obtained by the machine data acquisition unit 11 and the measurement data obtained by the measurement data acquisition unit 12. The correlation evaluation value is a value indicating the degree of strength of the correlation between the machine data and the measurement data. The correlation evaluation value is calculated based on one type of time series data among the machine data and one type of time series data among the measurement data. In addition, the two data for calculating the correlation evaluation value can be any combination as long as they are data that generate correlation. If they are the examples of the machine data and the measurement data described above, the data can be combined arbitrarily.

[0034] In addition, the correlation calculation unit 13 changes the time difference when any one of the machine data and the measurement data is shifted in the positive or negative direction of the time axis, and calculates the correlation evaluation value based on the changed time difference. That is, by changing the time difference, a plurality of correlation evaluation values ​​are obtained. In addition, the correlation calculation unit 13 calculates the correlation value at which the strength of the correlation between the machine data and the measurement data becomes the maximum, and calculates the correlation time difference, which is the time difference when the correlation value is obtained.

[0035] The synchronization data output unit 14 synchronizes the machine data and the measurement data based on the correlation time difference calculated by the correlation calculation unit 13. The synchronization data output unit 14 determines the machine data synchronized with the measurement data based on the correlation time difference as the synchronized machine data, and outputs it to the outside of the synchronization device 1. In addition, the synchronization data output unit 14 determines the measurement data synchronized with the machine data based on the correlation time difference as the synchronized measurement data, and outputs it to the outside of the synchronization device 1. In addition, each of the machine data and each of the measurement data can be associated with each other through the first and second time information and the information of the correlation time difference. Therefore, the synchronization process between different types of data can also be performed based on the information of the correlation time difference calculated by the correlation calculation unit 13.

[0036] Figure 2 1 is a diagram showing a configuration example of a drive system 500 including a drive device 510 having the function of the synchronizing device 1 according to the first embodiment. Figure 2 In the embodiment, the drive system 500 includes a blower 15 and a drive device 510 for driving the blower 15. The drive device 510 includes a control device 16, a measurement data acquisition unit 12, and an inspection terminal 18. The blower 15 includes an impeller 15a and a motor 15b mounted on the impeller 15a. The blower 15 and the inspection terminal 18 are electrically connected to the control device 16.

[0037] The control device 16 has an inverter (not shown). The control device 16 drives the motor 15b by supplying an electrical signal to the motor 15b. The impeller 15a is driven to rotate by the motor 15b and rotates at a fixed speed. The rotation speed of the impeller 15a changes by the electrical signal output by the control device 16, and the sound accompanying the air supply also changes at the same time.

[0038] The measurement data acquisition unit 12 measures the sound pressure of the sound emitted by at least one of the air blower 15 and the control device 16. An example of the measurement data acquisition unit 12 is a microphone. The measurement data acquisition unit 12 can be set at a predetermined fixed position in the structure of the drive system 500, or can be set at an arbitrary position according to the structure of the drive system 500. In addition, the measurement data acquisition unit 12 is preferably set near the source of the sound to be measured, that is, the air blower 15 or the control device 16. In addition, when a microphone is used as the measurement data acquisition unit 12, the microphone preferably has directivity. When the microphone has directivity, it is possible to reduce the surrounding noise mixed into the measurement data to be obtained.

[0039] The inspection terminal 18 is a terminal device that provides information required for inspection to an inspector of the blower 15. The inspection terminal 18 has a cable connector 21 and is connected to the control device 16 via a detachable cable 19a. The inspection terminal 18 obtains control data of the control device 16 via the cable 19a. Examples of the inspection terminal 18 are a smartphone, a tablet terminal, or a notebook personal computer.

[0040] The inspection terminal 18 is connected to the measurement data acquisition unit 12 via the cable 19b. The inspection terminal 18 acquires the sound pressure data measured by the measurement data acquisition unit 12.

[0041] As the cables 19a and 19b, cables used for data exchange of information terminals such as USB (Universal Serial Bus) cables, LAN (Local Area Network) cables, and SPI (Serial Peripheral Interface) communication cables can be used. The inspection terminal 18 has a connector of a required standard corresponding to the cables used for connection. In addition, the connection between the inspection terminal 18 and the measurement data acquisition unit 12 and between the inspection terminal 18 and the control device 16 can be connected by wireless communication without using cables.

[0042] In addition, a microphone built into the inspection terminal 18 may be used as the measurement data acquisition unit 12. In this case, the only equipment that needs to be carried during the inspection is the inspection terminal 18 and the cable 19a connecting the control device 16 and the inspection terminal 18, and the number of accessories can be reduced. This makes the inspection easier.

[0043] Figure 3 Yes means Figure 2 FIG. 1 is a block diagram showing an example of a functional structure of a driving device 510 according to Embodiment 1. Figure 3 As shown, the driving device 510 includes the measurement data acquisition unit 12, the control device 16, and the inspection terminal 18. The control device 16 includes a control unit 161, and the inspection terminal 18 includes a control unit 181, a storage unit 182, and a monitor 183.

[0044] The control unit 161 is a structural unit that centrally controls the entire control device 16. The control unit 161 includes a motor drive unit 162 and a device data acquisition unit 11. The motor drive unit 162 is a structural unit that generates an electrical signal for controlling the driving state of the motor connected to the control device 16 based on an instruction from a user of the drive device 510 or a higher-level controller.

[0045] The device data acquisition unit 11 is connected to the motor drive unit 162, and acquires data related to the driving of the control device 16 in time series. The device data acquisition unit 11 stores the device data collected in time series together with the first time information managed by the control device 16. The measurement data acquisition unit 12 stores the measurement data collected in time series together with the second time information.

[0046] In the inspection terminal 18, the control unit 181 is a structural unit that centrally controls the entire inspection terminal 18. The control unit 181 includes a synchronization unit 10, an abnormality diagnosis unit 184, a display unit 185, a machine data communication unit 186, and a measurement data communication unit 187. The synchronization unit 10 includes a correlation calculation unit 13 and a synchronization data output unit 14. Figure 3 In the structure of , the functions of the machine data acquisition unit 11 and the measurement data acquisition unit 12 exist outside the inspection terminal 18. Therefore, the machine data acquisition unit 11 and the measurement data acquisition unit 12 are eliminated from the synchronization unit 10. On the other hand, the synchronization unit 10 has the correlation calculation unit 13 and the synchronization data output unit 14, and thus has the function of outputting the synchronization machine data and the synchronization measurement data. Therefore, the synchronization unit 10 can be regarded as a component equivalent to the synchronization device 1.

[0047] The synchronization unit 10 is connected to the machine data acquisition unit 11 via the machine data communication unit 186. The machine data communication unit 186 acquires the machine data from the machine data acquisition unit 11 by communication. The synchronization unit 10 acquires the machine data from the machine data communication unit 186 at a required timing and stores it in the storage unit 182. In addition, the synchronization unit 10 is connected to the measurement data acquisition unit 12 via the measurement data communication unit 187. The measurement data communication unit 187 acquires the measurement data from the measurement data acquisition unit 12 by communication. The synchronization unit 10 acquires the measurement data from the measurement data communication unit 187 at a required timing and stores it in the storage unit 182.

[0048] The first time information managed by the control device 16 and the second time information managed by the measurement data acquisition unit 12 are different time information because they are managed by different systems. The synchronization unit 10 performs synchronization processing on the machine data and the measurement data based on the different time information according to the sequence described below based on the information of the time difference calculated by the correlation calculation unit 13. The machine data and the measurement data after the synchronization processing are outputted as synchronized machine data and synchronized measurement data by the synchronization data output unit 14, respectively.

[0049] The abnormality diagnosis unit 184 uses the synchronous machine data and the synchronous measurement data to diagnose the state of the blower 15 or the control device 16. For example, when the rotation speed of the motor 15b as the machine data changes from the stop to the speed specified by the user, but the sound pressure of the driving sound of the blower 15 as the measurement data does not change, it can be diagnosed that an abnormality has occurred in the blower 15. Although the command to start air supply is issued to the blower 15, there is no air supply sound and no air supply, so it can be diagnosed as described above. In the diagnosis of the abnormality diagnosis unit 184, as long as the abnormality diagnosis using the synchronous machine data and the synchronous measurement data is used, the required diagnosis can be performed according to the device structure or drive mode.

[0050] The display unit 185 visually displays the diagnosis result of the abnormal diagnosis implemented by the abnormal diagnosis unit 184. At this time, as a basis for the diagnosis result, the synchronized machine data and synchronized measurement data synchronized by the synchronization unit 10 can be displayed by the display unit 185 in the form of graphics or the like. By displaying the synchronized machine data and the synchronized measurement data, the inspector can have a deeper understanding of the cause of the diagnosis result. At this time, it is preferred that the synchronized machine data and the synchronized measurement data at the same time are arranged vertically or horizontally, and can be displayed graphically in parallel or superimposed. If the display as described above can be performed, the inspector can confirm the status of the machine in a more understandable form.

[0051] The storage unit 182 is connected to the control unit 181, and stores the machine data or the measurement data according to the request of the control unit 181, and stores the abnormality diagnosis results etc. as needed. The storage unit 182 has a ROM (Read Only Memory) or a RAM (Random Access Memory), and can also be configured using a flash memory, a HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[0052] The monitor 183 is controlled by the display unit 185. The monitor 183 displays the abnormality diagnosis result obtained by the abnormality diagnosis unit 184 to the inspector. An example of the monitor 183 is a liquid crystal display device. The monitor 183 can be configured as long as it can notify the inspector of the diagnosis result, and can be composed of an LED (Light Emitting Diode), a sound playback unit, etc.

[0053] Each function of the control unit 161, 181 can be implemented by software. In the case of implementation by software, the program constituting the software is installed in a computer that executes the functions of the control unit 161, 181. In addition, each function of the control unit 161, 181 is not limited to being implemented by software. Each function of the control unit 161, 181 can also be implemented using electronic circuits such as ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), CPLD (Complex Programmable Logic Device).

[0054] Next, regarding the synchronization processing in Implementation 1, Figure 2 and Figure 3 Based on the attached drawings, also refer to Figures 4 to 9 The following description will be given with reference to the accompanying drawings. Figure 4 This is a flowchart showing an example of the processing procedure of the synchronization process in the first embodiment. Figure 5 Yes means Figure 4 A waveform diagram illustrating an example of the machine data used. Figure 6 Yes means Figure 4 A waveform diagram of an example of measurement data used for the description. Figure 7 This is a block diagram showing an example of the functional configuration of the correlation calculation unit 13 in the first embodiment. Figure 8 Yes means Figure 4 A flowchart showing an example of a processing procedure of a correlation operation shown. Fig. 9 This is a waveform diagram used in the description of the output processing of the synchronous data in the first embodiment.

[0055] First, in step S11, the device data acquisition unit 11 acquires device data. Figure 5 , as an example of device data, current data indicating the magnitude of the current supplied from the motor drive unit 162 to the motor 15 b is shown. Figure 5 The horizontal axis represents time. Figure 5 In the figure, it is shown that at time t 0 The operation of the blower 15 is started, and after being temporarily driven with a constant current, at time t 1 The waveform when the operation is stopped. In addition, the waveform at time t 1 Thereafter, the polarity of the current is alternately reversed while the blower 15 is driven with a constant current waveform. 0 ,t 1 This is the aforementioned first moment information.

[0056] The current data is sampled by the machine data acquisition unit 11. In the first embodiment, the sampling frequency of the current data is set to 100 Hz. In addition, the sampling frequency does not necessarily have to be 100 Hz, and can be arbitrarily set according to the accuracy of the data, the performance of the hardware, etc. The machine data acquired in step S11 is input to the synchronization unit 10 via the machine data communication unit 186.

[0057] In step S12, the measurement data acquisition unit 12 acquires the measurement data. Figure 6 As an example of the measurement data measured by the measurement data acquisition unit 12, the diagram shows Figure 5 The present invention also provides sound pressure data indicating the driving sound of the blower 15 when the blower 15 is driven by the current waveform of FIG. Figure 6 The horizontal axis represents time. Figure 6 The sound pressure data in Figure 5 The same timing is obtained, but the time T is obtained 0 , T 1 is based on the second time information managed by the measurement data acquisition unit 12, and therefore has the same Figure 5 Different moments.

[0058] In Embodiment 1, the sampling frequency of the measurement data is set to 48 kHz. The sampling frequency does not necessarily have to be 48 kHz, and can be arbitrarily set according to data accuracy, hardware performance, etc. The measurement data acquired in step S12 is input to the synchronization unit 10 via the measurement data communication unit 187 .

[0059] In addition, the processing of step S11 and the processing of step S12 may be performed in reverse order or simultaneously. In addition, in the processing of steps S11 and S12, the data to be acquired need not be all the data to be synchronized, and a part of the data may be acquired. In this case, the data to be acquired only needs to be data of a section included in the data acquired at the same time. By limiting the data to be acquired to a part, the amount of computation in the processing in the correlation computing unit 13 described later can be reduced.

[0060] In step S13, the correlation calculation unit 13 performs correlation calculation using the machine data obtained by the process of step S11 and the measurement data obtained by the process of step S12. The correlation calculation unit 13 calculates a correlation evaluation value indicating the degree of strength of correlation between the machine data and the measurement data, and calculates a correlation value and a correlation time difference at which the degree of strength of correlation becomes the maximum based on the calculated plurality of correlation evaluation values. The correlation calculation will be described in more detail later.

[0061] In step S14, the synchronous data output unit 14 outputs synchronous data based on the result of the correlation operation performed in step S13. The synchronous data referred to here is the aforementioned synchronous device data and synchronous measurement data.

[0062] Next, Figure 4 The processing of step S13 in the flowchart of FIG. Figure 8 The flowchart is implemented by the correlation operation unit 13. The correlation operation unit 13 is as follows Figure 7 As shown, it includes a sampling adjustment unit 131 , a normalization unit 132 , a correlation evaluation value calculation unit 133 , a correlation value calculation unit 134 , and a correlation time difference determination unit 135 .

[0063] exist Figure 8 In step S131, the correlation operation unit 13 performs processing to make the sampling frequency of the device data and the sampling frequency of the measurement data the same. This processing is performed by Figure 7 The sampling adjustment unit 131 implements it.

[0064] As described above, in this example, the sampling frequency of the current data as the device data is 100 Hz, and the sampling frequency of the sound pressure data as the measurement data is 48 kHz. Therefore, the period of sampling 480 times from the sound pressure data is set as the sampling period, and one data is extracted for each sampling period. Through this process, the measurement data is decimated to 100 Hz.

[0065] In the above process, the sampling frequency, which is the inverse of the sampling period, is set to 100 Hz. This 100 Hz is the greatest common divisor of the sampling frequency of the machine data and the sampling frequency of the measurement data, but the sampling frequency may be set to a value smaller than the greatest common divisor. By setting the sampling frequency to a value smaller than the greatest common divisor, the amount of calculation after the sampling process in the correlation calculation unit 13 can be reduced.

[0066] As described above, the sampling adjustment unit 131 performs a decimation process corresponding to the sampling frequency of the machine data and the sampling frequency of the measurement data, and generates the machine data and the measurement data with the same sampling period. Here, the sampling period is the decimation period. In addition, the sampling adjustment unit 131 may replace the decimation process, or perform the decimation process and the interpolation process, and generate the machine data and the measurement data with the same sampling frequency.

[0067] In addition, the sampling adjustment unit 131 may perform a filter process using a digital filter on either or both of the machine data and the measurement data before the decimation process. Examples of digital filters are low-pass filters, high-pass filters, or band-pass filters. By performing the filter process, the influence of foldover noise generated when performing the decimation process or the interpolation process can be reduced. In addition, by performing the filter process, the influence of noise generated when the machine data acquisition unit 11 and the measurement data acquisition unit 12 acquire each data can be reduced.

[0068] exist Figure 8 In step S132, the normalization unit 132 normalizes the machine data and the measurement data whose sampling periods are the same. The normalization unit 132 reduces the offset components of the machine data and the measurement data, and sets the average values ​​of the machine data and the measurement data to 0. In addition, the normalization unit 132 multiplies one or both of the machine data and the measurement data by a certain value, thereby adjusting the ratio between the machine data and the measurement data.

[0069] By normalizing the normalization unit 132, when calculating the relevant evaluation value, the influence of the error caused by the machine data and the measurement data being different types of data can be reduced. In particular, the machine data and the measurement data can be data of different dimensions. In addition, the processing of step S131 and the processing of step S132 can also be reversed in order. That is, the processing can be performed in the order of the normalization unit 132 and the sampling adjustment unit 131. However, if the processing of the sampling adjustment unit 131 is performed first, the amount of calculation can be reduced. Therefore, in the case of wanting to reduce the calculation load, it is preferred to perform the processing of the sampling adjustment unit 131 first.

[0070] Next, in Figure 8In step S133, the correlation evaluation value calculation unit 133 calculates the correlation evaluation value indicating the degree of strength of the correlation between the machine data and the measurement data. In the next step S134, the correlation value calculation unit 134 calculates a plurality of correlation evaluation values ​​between the machine data and the measurement data, and calculates the correlation value based on the plurality of correlation evaluation values. That is, the correlation value calculation unit 134 calculates the correlation evaluation value having the largest strength of the correlation among the plurality of correlation evaluation values ​​as the correlation value. And in step S135, the correlation time difference determination unit 135 determines the correlation time difference based on the correlation value. The correlation time difference is information on the time difference when the correlation value having the largest strength of the correlation is obtained.

[0071] Next, the order of determining the relevant time difference is described in more detail. First, any one of the machine data and the measurement data is determined as the reference data. In addition, it is preferred to set the data with a shorter storage time among the machine data and the measurement data as the reference data. Below, for convenience, the reference data is referred to as "first data" and the data that is not the reference data is referred to as "second data".

[0072] Next, the correlation evaluation value between the data offset by the sampling period Δt with respect to the second data and the first data is calculated. Through this calculation, a correlation evaluation value column consisting of a plurality of correlation evaluation values ​​is obtained. When the correlation evaluation value is represented by V, the correlation evaluation value column can be represented as follows using integers M, N and the sampling period Δt.

[0073] {V(Δt×(-M)),…,V(Δt×(-1)),V(Δt×0),V(Δt×1),…,V(Δt×N)}

[0074] In the above-mentioned correlation evaluation value column, integers M and N are coefficients that determine the maximum time difference between the moment after the time axis is offset in the negative direction and the moment after the time axis is offset in the positive direction. The maximum time difference can be determined using the sampling time of each data. In addition, depending on the structure of the device, when the maximum time difference is predetermined, this value can be used.

[0075] exist Fig. 9 The lower side of Figure 6 The sound pressure data shown is shown as the first data. Fig. 9 The upper side of Figure 5 The current data shown is set as the second data, and shows the waveform of the entire current data after the waveform is shifted in the negative direction of the time axis by time Δt×n. Fig. 9 In the waveform of , the start of the current data is advanced by a time Δt×n relative to the sound pressure data serving as the reference data.

[0076] The correlation evaluation value calculated for the first data as the reference data and the second data after time shifting uses an evaluation index that can calculate the similarity between the two time series data. Specific examples of evaluation indexes are correlation coefficient, covariance, absolute error, square error, and Mahalanobis distance. In this case, the result after time shifting Δt×n is: Fig. 9 For intervals with only a single data point, the calculated value is 0, as in interval A of FIG.

[0077] In addition, when each data used in the calculation of the correlation evaluation value has a negative value, the absolute value of each data can be calculated and calculated. In the comparison of the correlation evaluation values, when the influence of the absolute value is high and the influence of the phase is low, the absolute value of each data is calculated and compared, thereby making it possible to perform appropriate correlation evaluation. In particular, when the frequency component of the sound pressure data is used as the measurement data, the frequency component of the sound pressure data does not include the phase, so it is preferable to use the absolute value of the machine data in the correlation calculation.

[0078] Next, the correlation evaluation value at which the strength of correlation becomes the largest among the calculated correlation evaluation value columns is set as the correlation value, and the value of the time difference when the correlation value is obtained is set as the correlation time difference. The correlation evaluation value at which the strength of correlation becomes the largest is the value at which the correlation evaluation value becomes the largest or the smallest. In addition, the correlation evaluation value becomes the largest or the smallest depending on which value is used as the correlation evaluation value.

[0079] The relevant time difference is obtained through the above-mentioned sequence. In addition, in the first embodiment, the relevant time difference is obtained by calculating the time difference at which the relevant evaluation value becomes the maximum or minimum, but the relevant operation unit 13 only needs to obtain the time difference with the strongest correlation, that is, the relevant time difference, and the relevant time difference can be calculated by a method that does not obtain the relevant evaluation value. For example, the relevant time difference can be obtained by calculating the phase difference using Fourier transform, or it can be obtained using a convolutional neural network. The synchronous data output unit 14 can output the synchronous machine data and the synchronous measurement data based on the relevant time difference. The synchronous machine data is the machine data synchronized with the measurement data. In addition, the synchronous measurement data is the measurement data synchronized with the machine data. In the above example, the sound pressure data is set as the reference data, so that the current data is offset by the amount of the relevant time difference relative to the reference data, thereby enabling the sound pressure data and the current data to be synchronized.

[0080] According to the synchronization device involved in the first embodiment, the machine data and the measurement data based on different time information obtained by different systems can be synchronized based on the correlation operation between the two data. Therefore, as a synchronization device, even if the two data to be synchronized are based on different time information obtained by different systems, the two data can be synchronized and output.

[0081] In addition, according to the synchronization device according to the first embodiment, it is not necessary to perform synchronization in order to calculate the correlation, and a feature that serves as a synchronization reference is set in the synchronization device. This makes it possible to perform synchronization processing between machine data and measurement data more easily. In particular, it is not necessary to store normal machine data or normal measurement data for synchronization, so synchronization processing can be performed from the initial operation.

[0082] The correlation between the machine data and the measured data used by the synchronization device involved in the first embodiment is universal. Therefore, in a machine equipped with the synchronization device involved in the first embodiment, multiple time series of data can be output synchronously with each other. For example, the relationship between the current value of the motor and the magnitude of the driving sound emitted from the motor is the following relationship, that is, when the current flows in the motor, the motor drives and the driving sound becomes louder, and when the current does not flow in the motor, the motor stops and the driving sound becomes smaller. This relationship does not depend on the driving mode of the motor. Therefore, in a large number of machines with motors, even if the structure or driving mode of the machine is changed, multiple time series of data can be output synchronously with each other.

[0083] The synchronization device according to the first embodiment performs synchronization processing by performing correlation operations between different machine data and measurement data obtained in different systems. As a result, the inspection terminal equipped with the synchronization device according to the first embodiment can diagnose abnormalities of the blower based on the synchronized machine data and synchronized measurement data output by the synchronization device. As a result, the inspection terminal according to the first embodiment can diagnose abnormalities using machine data and measurement data obtained by different devices without providing a high-speed network for synchronization. In addition, the inspection terminal according to the first embodiment can diagnose abnormalities without resetting for synchronization when the drive mode of the blower or the machine structure of the blower changes.

[0084] In addition, the inspection terminal involved in embodiment 1 brings the microphone close to the candidate part of the device that is the cause of the abnormal noise, records the abnormal noise, and can display the sound pressure data related to the abnormal noise synchronously with the machine data when the abnormal noise occurs. As a result, when an abnormal noise occurs in the blower, the user can visualize the machine data and the abnormal noise. Moreover, the inspector can infer the cause of the abnormal noise by clearly observing the relationship between the machine data and the abnormal noise. In particular, the machine data and the measurement data at the same time are displayed side by side in a straight line in the same direction vertically or horizontally, so that the status of the machine can be presented in a more understandable form.

[0085] In addition, the inspection terminal according to Embodiment 1 can synchronize the input device data with the measurement data. Therefore, even when the inspection terminal is installed in an existing device later, the same effect as when the device is installed at the factory can be obtained. In particular, by building the measurement data acquisition unit into the inspection terminal, the device status of the existing device can be visualized more easily.

[0086] In addition, the inspection terminal according to the first embodiment can inspect a plurality of devices at one terminal, and the inspection terminal can be connected to the devices sequentially via cables to perform the inspection. This can reduce the number of inspection terminals required.

[0087] Furthermore, the synchronization device according to Embodiment 1 may include a sampling adjustment unit. The sampling adjustment unit performs sampling adjustment, thereby being able to calculate a correlation evaluation value and perform synchronization processing even when the device data and the measurement data have different sampling periods.

[0088] Furthermore, the synchronization device according to Embodiment 1 may include a normalization unit. The normalization unit normalizes data before calculating the correlation evaluation value, thereby reducing the influence of errors that occur when the device data and the measurement data are in different units.

[0089] Implementation method 2.

[0090] Fig.10 FIG. 2 is a block diagram showing an example of a functional structure of a synchronization device 2 according to Embodiment 2. Figure 1 Compared with the structure of the synchronization device 1 involved in the embodiment 1 shown in FIG. 1 , the correlation operation unit 13 is replaced by the correlation operation unit 23. In the embodiment 2, the time change of the spectrum is used as the data for synchronization. Therefore, the correlation operation unit 23 performs correlation operation after frequency conversion on the data. In addition, with respect to other structural parts, Figure 1 The same or equivalent structural parts are marked with Figure 1 The same reference numerals are used and repeated descriptions are omitted.

[0091] Fig.11 is a block diagram showing an example of the functional structure of the correlation operation unit 23 in Embodiment 2. If the correlation operation unit 23 in Embodiment 2 is compared with Figure 7 the structure of the correlation operation unit 13 in Embodiment 1 shown, a frequency conversion unit 234, a frequency selection unit 235, and an envelope processing unit 236 are provided between the sampling adjustment unit 131 and the normalization unit 132. Regarding other structural units, they are the same as or equivalent to Figure 7 the structural units shown, and for the same or equivalent structural units, the same reference numerals are used and repeated descriptions are omitted. Figure 7 The same reference numerals are used and repeated descriptions are omitted.

[0092] In Fig.11 , the sampling adjustment unit 131 performs decimation processing corresponding to the sampling frequency of the machine data and the sampling frequency of the measurement data, and generates machine data and measurement data with the same sampling period. Regarding the data among the machine data and the measurement data with the same sampling period that do not perform frequency conversion processing, they are input to the normalization unit 132. On the other hand, regarding the data that perform frequency conversion processing, they are input to the frequency conversion unit 234.

[0093] The frequency conversion unit 234 performs processing to transform at least one time series data among the machine data or the measurement data into a time series spectrum. As a method for transforming into a time series spectrum, short-time fast Fourier transform (STFFT), wavelet transform, discrete cosine transform, cepstrum, etc. can be used. The time series spectrum transformed by the frequency conversion unit 234 can be used when being displayed on the display unit 287 described later.

[0094] The frequency selection unit 235 selects the spectrum in a predetermined frequency range from the time series spectrum calculated by the frequency conversion unit 234, and sets it as the machine data or the measurement data used for the correlation operation. Here, the frequency range used for selection preferably includes the frequencies related to the operation of the machine device. The resonance frequency of the machine device, the rotation frequency of the device, and the frequencies that are integer multiples of these frequencies are an example. The frequency range used for selection can be set in advance or determined according to the obtained machine data or measurement data.

[0095] When wavelet transform or discrete cosine transform is used in the frequency transform unit 234, the order of the processing of the frequency transform unit 234 and the processing of the frequency selector 235 can be reversed. In addition, when the processing of the frequency selector 235 is performed first, the calculated time series spectrum cannot be used for display, but the operation of the frequency transform can be limited to the frequency range used for selection, thereby obtaining an effect of reducing the amount of calculation.

[0096] The envelope processing unit 236 detects the envelope of the time series data of the frequency spectrum in the selected frequency range. Examples of envelope processing are low-pass filter processing or Hilbert transform. In addition, the envelope processing unit 236 may also be omitted.

[0097] Fig.12 2 is a diagram showing a configuration example of a data analysis system 520 including a data analysis device 281 having the function of the synchronization device 2 according to the second embodiment. Fig.12 In the data analysis system 520, the four-axis robot 25, the controller 261, the servo drivers 262 to 265, the acceleration sensor 271, the camera 272, the recorder 291, the database server 293, the data analysis device 281, and the visualization terminal 282 are connected to the recorder 291 via the database server 293. The database server 293 may be a cloud server configured on the network 292.

[0098] The 4-axis robot 25 is a device that performs operations based on the instructions of the controller 261. The 4-axis robot 25 includes 4 motors (not shown) that drive each of the 4 axes, and machine elements that transmit power to each axis. In addition, the actual 4-axis robot includes many parts, but for the sake of simplicity, the following parts are shown in the figure. Fig.12 Only some of the structural elements are shown.

[0099] The controller 261 is connected to the 4-axis robot 25 through the servo drivers 262 to 265, and centrally controls the driving of the 4-axis robot 25. The controller 261 manages the aforementioned first moment information. In addition, the controller 261 has the following function, that is, based on the first moment information, it gives instructions to the necessary axes, thereby driving the 4-axis robot 25 according to the action desired by the commander.

[0100] The servo drivers 262 to 265 are connected correspondingly to the motors of the four-axis robot 25 , and generate electrical signals as commands for driving the four-axis robot 25 in accordance with commands from the controller 261 , and apply the electrical signals to the motors of the respective axes.

[0101] The acceleration sensor 271 is attached to the movable part of the wrist of the four-axis robot 25, and measures the vibration generated in the four-axis robot 25 by driving the motors of the axes in a time series. As the acceleration sensor 271, a three-axis acceleration sensor can be used.

[0102] The camera 272 is an industrial camera that obtains the motion of the four-axis robot 25 by intermittently or continuously photographing the four-axis robot 25. The camera 272 manages the second time information mentioned above. In addition, the camera 272 photographs the motion of the four-axis robot 25 and its motion sound based on the second time information, and stores the photographed image data and sound data as video data. The video data is sent to the recorder 291.

[0103] The recorder 291 is connected to the acceleration sensor 271 via the cable 274, and is connected to the camera 272 via the cable 275. The recorder 291 manages the third time information. The recorder 291 accesses each connected device sequentially or simultaneously, obtains the rotation speed data of each axis in the four-axis robot 25 as machine data together with the time information, and sends it to the database server 293. In addition, the recorder 291 obtains the measurement value of the acceleration sensor 271 and the video data of the camera 272 as measurement data together with the time information of these data, and sends them to the database server 293.

[0104] In addition, Fig.12 In the structure of , the acceleration sensor 271 does not have time information. Therefore, the recorder 291 adds the third time information to the measurement data of the acceleration sensor 271 and sends it to the database server 293.

[0105] The database server 293 stores the device data obtained by the recorder 291 and the measurement data with the time information in the database. An example of the database server 293 is RDBMS (Relational DataBase Management System), and another example of the database server 293 is Not only SQL (Structured Query Language). When the database server 293 stores the data, it stores the data together with the fourth time information managed by the database server 293.

[0106] The data analysis device 281 is a device that analyzes and learns the state of the four-axis robot 25 using the data of the four-axis robot 25 accumulated in the database. Fig.13 Yes means Fig.121 is a block diagram showing an example of the functional structure of the data analysis device 281. The data analysis device 281 includes an input unit 283, a database communication unit 284, a synchronization device 2, a data analysis unit 285, and an output unit 286.

[0107] The input unit 283 obtains the device data and measurement data that are the objects of data analysis. The device data and measurement data that are the objects of data analysis are set by the user via the input unit 283. At this time, in addition to the type of data that are the objects of analysis, the range of the time of obtaining the data can also be set. By specifying the time of obtaining the data, the amount of data that are the objects of analysis can be reduced. As a result, the processing time can be reduced.

[0108] The database communication unit 284 uses SQL and other means to query the database server 293 for the machine data and measurement data to be analyzed, and obtains the required data. When the range of the acquisition time of the data is set, the data to be analyzed is obtained based on the fourth time information managed by the database server 293. The database server 293 assigns common time information to all data, thereby enabling appropriate selection of data. In addition, when setting the range of the acquisition time, a range larger than the range of the specified time can be set to obtain data. If it is performed in the above manner, even if part of some data is lost due to the relevant time difference obtained during the synchronization process, if the range of the acquisition time is set in anticipation of this situation, it is possible to provide the user with data without loss including the range of the specified acquisition time.

[0109] The synchronization device 2 can perform synchronization processing by using various combinations of rotation speed data based on the first time information, video data based on the second time information, and acceleration data based on the third time information. Here, when the rotation speed data based on the first time information is synchronized as the machine data, the time t 0 The machine data can be 0 The rotation speed of the four axes in 1 (t 0 ),ω 2 (t 0 ),ω 3 (t 0 ),ω 4 (t 0 ) has the largest absolute value of the rotational speed i＝1、2、3、4 |ω i (t 0 )| as time t 0As a result, for example, when performing correlation calculations with sound data in video data as measurement data, it is possible to suppress the calculation of inappropriate correlation evaluation values ​​due to the influence of drive sounds generated by the actions of other axes. Generally speaking, it is considered that even if there is only one motor being driven, a drive sound will be generated. Therefore, by selecting the maximum value among the four axes, it is possible to calculate a correlation evaluation value that takes into account the influence of drive sounds generated by the actions of other axes.

[0110] The data analysis unit 285 analyzes the motion of the 4-axis robot 25 using the synchronous machine data and the synchronous measurement data. The data analysis unit 285 analyzes the data for the 4-axis robot 25 from the perspective of abnormality diagnosis or predictive maintenance. Existing methods such as machine learning or statistical analysis can be used for these analyses. The results analyzed in the data analysis unit 285 are output or displayed via the output unit 286.

[0111] The visualization terminal 282 is a terminal device for visualizing and displaying the state of the four-axis robot 25. Examples of the visualization terminal 282 are a display, a smartphone, a tablet terminal, or a notebook personal computer. The visualization terminal 282 displays the state of the four-axis robot 25 using the data of the four-axis robot 25 accumulated in the database server 293 and the analysis results of the data analysis device 281.

[0112] Fig.14 2 is a block diagram showing an example of the functional structure of the visualization terminal 282 according to the second embodiment. Fig.14 In the Fig.13 The structural elements shown in the figure are common to the structural elements, and are marked with Fig.13 The same reference numerals are used throughout and duplicate descriptions are omitted.

[0113] The visualization terminal 282 includes an input unit 283 , a database communication unit 284 , the synchronization device 2 , and a display unit 287 .

[0114] The synchronization device 2 acquires the synchronization device data and the synchronization measurement data stored in the database server 293 via the database communication unit 284. The synchronization device 2 outputs the synchronization device data and the synchronization measurement data, and the display unit 287 displays the synchronization device data and the synchronization measurement data output from the synchronization device 2.

[0115] Fig.15 FIG. 2 is a diagram showing a display example of the visualization terminal 282 based on the analysis result of the data analysis device 281 in the second embodiment. Fig.15 The lower side of shows the synchronous machine data, that is, the speed data of the first axis in the 4-axis robot 25. Fig.15The upper side of the synchronous measurement data, that is, the frequency conversion result of the sound data in the video data is graphically displayed. 0 , time t 1 It is the time after synchronization. The synchronized machine data and synchronized measurement data are the same time. Fig.15 As shown in FIG. 1 , the machine data and the measured data at the same time are displayed side by side in a straight line in the same direction vertically or horizontally, thereby being able to present the machine status in a more understandable form. In particular, by simultaneously displaying the distribution of the frequency components of the machine data and the sound data at the same time, it becomes easier to estimate the cause of an abnormal sound or the like.

[0116] In the second embodiment, the synchronization device 2 is arranged in the data analysis device 281 or the visualization terminal 282, but it may be arranged in the recorder 291 or between the recorder 291 and the database server 293. In this case, the database server 293 stores the synchronized machine data and measurement data in the database, thereby eliminating the need for synchronization processing in the data analysis device 281 or the visualization terminal 282, thereby reducing processing and achieving an effect of faster response to user operations.

[0117] The synchronization device according to the second embodiment performs frequency conversion on at least one of the machine data or the measurement data, obtains the frequency spectrum of the time series, and performs correlation operation. As a result, the data to be subjected to correlation operation can be limited to data of a specific frequency, thereby improving the synchronization accuracy. In particular, by performing correlation operation with the frequency representing the operation of the machine, such as the resonance frequency of the machine device, a higher-precision synchronization process can be performed.

[0118] In addition, the synchronization device according to Embodiment 2 performs envelope processing when performing frequency conversion, thereby being able to reduce the influence of errors in measurement data when performing correlation calculations.

[0119] The synchronization device according to Embodiment 2 can synchronize video data and the measured value of the acceleration sensor via audio data. This allows the user to easily understand the phenomenon that occurs when the acceleration sensor becomes abnormal by using synchronized video data of the phenomenon that occurs in the entire device.

[0120] In addition, the data analysis device involved in the second embodiment stores the rotation speed data corresponding to the four axes, the measured values ​​of the acceleration sensor provided at the wrist, and the video data during driving together with the respective time information in the database server. As a result, there is no need for a structure for synchronizing the state of the four-axis robot at all times, and the driving data can be accumulated with a simpler structure. In particular, the data accumulation environment can be easily introduced into the existing equipment.

[0121] In addition, the data analysis device involved in embodiment 2 can synchronize machine data and measurement data by using a synchronization device provided in the analysis device and the visualization terminal. As a result, analysis and visualization can be performed based on high-precision data when utilizing the database such as analysis and visualization. For example, the user can overlook the data by using an existing method for comprehensively displaying the data accumulated in the database. At this time, by using synchronized machine data and synchronized measurement data, the cause-effect relationship of the phenomenon occurring by the combination of each data becomes clearer. In addition, the user can perform analysis, learning, etc. on the data accumulated in the database. At this time, by using the synchronized data, the cause-effect relationship of the phenomenon occurring by the combination of data can be introduced into the analysis and learning, and a more accurate result can be obtained.

[0122] Implementation method 3.

[0123] Fig.16 FIG. 1 is a block diagram showing an example of a functional structure of a synchronization device 3 according to Embodiment 3. Figure 1 Compared with the structure of the synchronization device 1 involved in the embodiment 1 shown in FIG. 1 , the correlation operation unit 13 is replaced by a correlation operation unit 33. In the embodiment 3, as in the embodiment 2, the time variation of the spectrum is used as the data for synchronization. Therefore, the correlation operation unit 33 performs a correlation operation after frequency conversion on the data. In addition, the correlation operation unit 33 automatically calculates the frequency used for the correlation operation by peak extraction. In addition, with respect to other structural units, the correlation operation unit 13 is replaced by a correlation operation unit 33. Figure 1 The same or equivalent structural parts are marked with Figure 1 The same reference numerals are used and repeated descriptions are omitted.

[0124] Fig.17 1 is a block diagram showing an example of the functional structure of the correlation operation unit 33 in the third embodiment. Fig.11 Compared with the structure of the correlation operation unit 23 in the second embodiment shown in FIG. 1 , a peak extraction unit 337 is provided between the frequency conversion unit 234 and the frequency selection unit 235. In addition, the normalization unit 132 is replaced by the normalization unit 332, the correlation evaluation value operation unit 133 is replaced by the correlation evaluation value operation unit 333, the correlation value operation unit 134 is replaced by the correlation value operation unit 334, and the correlation time difference determination unit 135 is replaced by the correlation time difference determination unit 335. As for other structural units, the same as Fig.11 The same or equivalent structural parts are marked with Fig.11 The same reference numerals are used and repeated descriptions are omitted.

[0125] exist Fig.17 In the process, the peak extraction unit 337 extracts a frequency greater than or equal to 1 at which the spectrum becomes a peak, for the machine data or measurement data that undergoes frequency conversion. Here, this frequency is referred to as the "peak frequency". The peak frequency averages the spectrum of the time series, which is the result of the conversion by the frequency conversion unit 234, in the time direction. When it is regarded as the average power for each frequency, the frequency at which the average power becomes a peak can be used. Alternatively, the peak value of the spectrum calculated by performing FFT (Fast Fourier Transform) processing on the entire data can be calculated as the peak frequency. When calculating the peak frequency, if the result of the frequency conversion unit 234 is not used, the order of the processing by the peak extraction unit 337 and the processing by the frequency conversion unit 234 can be swapped.

[0126] The normalization unit 332 normalizes the device data and the measurement data whose sampling periods are the same by the same method as the normalization unit 132. However, when two or more frequencies are extracted by the peak extraction unit 337, different coefficients are multiplied for each frequency so that all scales become constant.

[0127] The correlation evaluation value calculation unit 333 and the correlation value calculation unit 334 calculate the correlation evaluation value and the correlation value by the same method as the correlation evaluation value calculation unit 133 and the correlation value calculation unit 134. In addition, the correlation time difference determination unit 335 determines the correlation time difference by the same method as the correlation time difference determination unit 135. However, when two or more frequencies are specified by the peak extraction unit 337, the correlation evaluation value calculation unit 333 calculates the correlation evaluation value column for each frequency. The correlation value calculation unit 334 calculates the correlation evaluation value with the largest correlation strength among the correlation evaluation values ​​of all frequencies as the correlation value. In addition, the correlation time difference determination unit 335 determines the time difference corresponding to the correlation value as the correlation time difference.

[0128] Furthermore, the frequency selected by the peak extraction unit 337 or the frequency selection unit 235 may be stored in the memory of the synchronization device 3. Thus, in the second and subsequent processing, the frequency stored in the memory is selected, thereby omitting the processing by the peak extraction unit 337 and the frequency selection unit 235.

[0129] Fig.18 1 is a diagram showing a configuration example of a simulation system 530 including a simulation terminal 38 having the function of the synchronization device 3 according to the third embodiment. Fig.18 In the Fig.12 Structural elements common to any of the structural elements shown are marked with Fig.12 The same reference numerals are used throughout and duplicate descriptions are omitted.

[0130] exist Fig.18 In the simulation system 530 , there is a four-axis robot 25 , a controller 261 , servo drivers 262 to 265 , an acceleration sensor 271 , a recorder 291 , and a simulation terminal 38 .

[0131] The simulation terminal 38 is connected to the controller 261 and the recorder 291. The simulation terminal 38 is a terminal device for performing a driving simulation on the 4-axis robot 25. The driving simulation can be implemented by the command data and operation data such as the position, rotation speed, current value, etc. of each motor of the 4-axis robot 25 based on the first moment information and the acceleration data of the 4-axis robot 25 based on the third moment information. The command data or operation data such as the position, rotation speed, current value, etc. of each motor is obtained from the controller 261. The acceleration data of the 4-axis robot 25 is obtained from the recorder 291. As the simulation terminal 38, a display, a smart phone, a tablet terminal, or a notebook personal computer can be used.

[0132] Fig.19 1 is a block diagram showing an example of the functional structure of the simulation terminal 38 according to the third embodiment. Fig.19 In the example, the simulation terminal 38 includes a command operation data communication unit 381 , an acceleration data communication unit 382 , a synchronization device 3 , a simulation unit 383 , and a display unit 384 .

[0133] In the simulation terminal 38, the synchronization device 3 performs synchronization processing by performing correlation calculation between the command data or operation data indicating the action of the machine and the acceleration data indicating the state of the machine. The simulation unit 383 performs a drive simulation of the four-axis robot 25 using the synchronization machine data and the synchronization measurement data output by the synchronization device 3. The simulation result of the simulation unit 383 is displayed to the user by the display unit 384.

[0134] The synchronization device according to the third embodiment extracts one or more frequencies that are peak frequencies, normalizes the extracted frequencies so that the ratios are the same, and then calculates the correlation time difference at which the correlation intensity becomes the maximum. Thus, for example, for a device whose peak frequency of a driving sound generated by a machine resonance or the like is unknown, it is possible to perform synchronization processing with good accuracy between the machine data and the sound data without specifying the frequency. When calculating the peak value for a device whose peak frequency is unknown, there is a problem that if the peak value is obtained for a sound in a certain interval by FFT or the like, the peak frequency is erroneously detected due to stable noise such as the sound of surrounding devices. To address this problem, the synchronization device according to the third embodiment extracts one or more frequencies that are peak frequencies of the spectrum, and performs correlation calculation between the extracted frequency data and the machine data representing the operation of the machine. Thus, it is possible to exclude the surrounding noise that is not related to the operation of the machine, and thus it is possible to reliably obtain the peak frequency of the driving sound.

[0135] In addition, the simulation terminal involved in Implementation 3 can synchronously input the command data and operation data obtained by the controller and the acceleration data obtained by the acceleration sensor into the simulator. Thus, the estimated value and the measured value of the acceleration sensor obtained by simulation can be compared, and the appropriateness of the simulation model can be verified. Since the appropriateness of the simulation model can be verified, it is easy to implement updates to the latest state of the simulation model. In addition, when implementing these verifications and updates, the synchronization processing between the data is performed by the synchronization device, so the simulation unit only needs to perform simulations other than the synchronization processing. Thus, a simulation with good accuracy can be performed by a simpler device. In addition, when the function of the digital twin is realized by the simulation terminal, the appropriateness verification of the digital twin can also be easily implemented.

[0136] Furthermore, the configuration described in the above embodiment is merely an example, and may be combined with other known technologies, the embodiments may be combined with each other, and part of the configuration may be omitted or changed without departing from the gist.

[0137] Description of the label

[0138] 1, 2, 3 synchronization device, 10 synchronization unit, 11 machine data acquisition unit, 12 measurement data acquisition unit, 13, 23, 33 correlation calculation unit, 14 synchronization data output unit, 15 blower, 15a impeller, 15b motor, 16 control device, 18 inspection terminal, 19a, 19b, 274, 275 cable, 21 cable connector, 25 4-axis robot, 38 simulation terminal, 131 sampling adjustment unit, 132, 332 normalization unit, 133, 333 correlation evaluation value calculation unit, 134, 334 correlation value calculation unit, 135, 335 correlation time difference determination unit, 161, 181 control unit, 162 motor drive unit, 182 storage unit, 183 monitor, 184 abnormality diagnosis unit, 185, 287, 384 display unit, 186 machine data communication unit, 187 measurement data communication unit, 234 frequency conversion unit, 235 frequency selection unit, 236 envelope processing unit , 261 controller, 262~265 servo driver, 271 acceleration sensor, 272 camera, 281 data analysis device, 282 visualization terminal, 283 input unit, 284 database communication unit, 285 data analysis unit, 286 output unit, 291 recorder, 292 network, 293 database server, 337 peak extraction unit, 381 instruction operation data communication unit, 382 acceleration data communication unit, 383 simulation unit, 500 drive system, 510 drive device, 520 data analysis system, 530 simulation system.

Claims

1. A synchronization device, It is characterized in that have: a machine data acquisition unit that acquires time-series information related to driving of the machine and obtains information acquired at a time indicated by the first time information as machine data; a measurement data acquisition unit that acquires time-series information related to the state of the device and includes information including a magnitude of a sound or vibration acquired at a time indicated by second time information different from the first time information as measurement data; a correlation calculation unit that calculates a correlation time difference, which is a time difference when the strength of the correlation between the absolute value of the machine data and the frequency component of the measurement data becomes maximum, based on the machine data, the measurement data, and the time difference when any one of the machine data and the measurement data is shifted in the positive or negative direction of the time axis; as well as The synchronous data output unit outputs the device data synchronized with the measurement data based on the relevant time difference as the synchronous device data, and outputs the measurement data synchronized with the device data based on the relevant time difference as the synchronous measurement data.

2. The synchronization device according to claim 1, It is characterized in that The correlation calculation unit includes a normalization unit that normalizes the device data and the measurement data used for calculation of the correlation value.

3. The synchronization device according to claim 1, It is characterized in that The correlation operation unit includes a peak extraction unit that obtains a frequency at which a spectrum of the measurement data becomes a peak based on the measurement data. The strength of the correlation is calculated based on the temporal change of the frequency component that becomes a peak in the frequency spectrum of the measurement data and the device data.

4. The synchronization device according to claim 3, It is characterized in that The machine is a machine driven by an electric motor.

5. The synchronization device according to any one of claims 1 to 3, It is characterized in that The machine is a machine driven by an electric motor, The machine data is data related to a rotation angle, speed, current, or force of the motor. 6 . A storage medium storing a program for causing a computer to implement the function of the synchronization device according to claim 1 .

7. A storage medium storing a program for causing a computer to execute a processing sequence, The processing sequence includes: In a first step, time series information related to the driving of the machine is acquired, and information acquired at a time indicated by first time information is obtained as machine data; A second step of acquiring time series information related to the state of the device and including information including the magnitude of sound or vibration acquired at a time indicated by second time information different from the first time information as measurement data; A third step of calculating a correlation value at which the strength of the correlation between the absolute value of the machine data and the frequency component of the measurement data becomes maximum, based on the machine data, the measurement data, and a time difference when any one of the machine data and the measurement data is shifted in the positive or negative direction of the time axis; Step 4, calculating the time difference when the correlation value is obtained, that is, the correlation time difference; and In a fifth step, the device data synchronized with the measurement data based on the relevant time difference is determined as the synchronized device data, and the measurement data synchronized with the device data based on the relevant time difference is determined as the synchronized measurement data.

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