Diagnostic system and diagnostic method

Abstract

To provide a diagnostic system that can efficiently extract steady-state data used for diagnosis in a short period of time.SOLUTION: A diagnostic system according to the present invention comprises: a measurement unit that measures and acquires time-series data from an appliance which is a diagnosis target; a waveform division unit that generates a plurality of divided waveforms by dividing the waveform of the time-series data into a plurality of waveforms with respect to the time base; a stationarity index conversion unit that calculates a stationarity index, which is an index for determining whether the time-series data is in a steady state for each of the plurality of divided waveforms generated by the waveform division unit; a steady-data extracting unit that obtains an extreme value of the stationarity index calculated by the stationarity index conversion unit, and obtains the divided waveforms, including the measured value corresponding to the extreme value, as steady-state data; and a steady-data extracting unit which diagnoses the state of the appliance by using the divided waveforms obtained as data in the steady state to provide data to a diagnosing unit.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a diagnostic device and a diagnostic method for diagnosing the state of a device. [Background technology]

[0002] Electric motors and other devices are used not only in production facilities but also in automobiles, including electric and hybrid vehicles, railway vehicles, and construction machinery, among other fields. When a specific piece of equipment in these facilities suddenly breaks down, unplanned repair or replacement work becomes necessary, resulting in a drop in the equipment's operating rate. This can lead to damage or inconvenience, such as a reassessment of production plans or a drop in operating rate in the case of production facilities, or the unavailability of vehicles in the case of vehicles.

[0003] Therefore, by investigating the signs of equipment failure, it is possible to prepare replacement parts in advance for equipment that is likely to fail and plan repairs in advance, thereby minimizing declines in equipment availability and revisions to production plans.The same applies to equipment attached to motors, such as inverters (power converters) that supply power to production facilities and vehicles.In other words, by investigating the signs of equipment failure, it is possible to prepare replacement parts in advance for equipment that is likely to fail and plan repairs in advance, thereby minimizing declines in equipment availability and revisions to production plans.

[0004] One method for investigating signs of equipment failure is to acquire time-series data such as current and vibration from the equipment and analyze this data to diagnose the equipment. Frequency analysis is often used as a data analysis technique. For example, a method is used to diagnose the condition of a motor based on the results of frequency analysis of measured values ​​such as motor current and vibration. In particular, the method that uses motor current is called MCSA (Motor Current Signature Analysis).

[0005] To apply this method, a steady-state current waveform is required. However, it can be difficult to obtain a steady-state current waveform. For example, since a vehicle undergoes rapid state changes due to acceleration and deceleration, the steady-state current waveform lasts for a short time and appears infrequently. Also, even in production equipment where state changes are rapid, the steady-state current waveform may last for a short time and appear infrequently. If a steady-state current waveform cannot be obtained, it is difficult to perform diagnosis using frequency analysis with high accuracy.

[0006] Therefore, a common method is to preprocess the measurement data before diagnosis, such as extracting only the measurement data suitable for diagnosis. This preprocessing makes it possible to extract measurement data in a steady state, enabling diagnosis by frequency analysis.

[0007] Patent Document 1 describes an example of a method for extracting data to be used for diagnosis through preprocessing. In the invention described in Patent Document 1, a steady state is defined using a preset reference value (threshold value), and measurement data from the start time of the steady state to the time when a predetermined time has elapsed after the end of the steady state is extracted as diagnostic data. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2022-183666 Summary of the Invention [Problem to be solved by the invention]

[0009] In conventional technologies, such as the invention described in Patent Document 1, a steady state of measurement data is determined using a preset threshold (reference value), and this threshold is used as a trigger to extract data to be used for diagnosis (steady-state measurement data). However, this conventional technology has the problem that setting the threshold requires trial and error, and adjusting the threshold takes time. For example, even if an attempt is made to set a trigger threshold based on the rated value of the target device, it is not easy to set the threshold because the operating conditions of the device vary depending on the usage state. Furthermore, if an appropriate threshold is not set, there is the problem that data that is changing may be mistakenly extracted as steady-state data, or the threshold may not serve as a trigger and diagnostic data may not be extracted.

[0010] An object of the present invention is to provide a diagnostic device and a diagnostic method that can efficiently extract steady-state data to be used for diagnosis in a short time. [Means for solving the problem]

[0011] A diagnostic device according to the present invention includes a measurement unit that measures and acquires time-series data from a device to be diagnosed; a waveform division unit that generates a plurality of divided waveforms by dividing a waveform of the time-series data into a plurality of waveforms along a time axis; a stationarity index conversion unit that calculates a stationarity index, which is an index for determining whether the time-series data is in a steady state, for each of the plurality of divided waveforms generated by the waveform division unit; a steady-state data extraction unit that calculates extreme values ​​of the stationarity index calculated by the stationarity index conversion unit and calculates the divided waveforms including measurement values ​​corresponding to the extreme values ​​as steady-state data; and a diagnostic unit that diagnoses the state of the device using the divided waveforms calculated by the steady-state data extraction unit as the steady-state data.

[0012] A diagnostic method according to the present invention includes a measurement step of measuring and acquiring time-series data from a device to be diagnosed; a waveform division step of dividing the waveform of the time-series data into a plurality of waveforms along a time axis to generate a plurality of divided waveforms; a stationarity index conversion step of calculating, for each of the plurality of divided waveforms generated in the waveform division step, a stationarity index which is an index for determining whether the time-series data is in a steady state; a steady-state data extraction step of calculating, as steady-state data, extreme values ​​of the stationarity index calculated in the stationarity index conversion step, and calculating the divided waveforms including measurement values ​​corresponding to the extreme values; and a diagnostic step of diagnosing the state of the device using the divided waveforms calculated as the steady-state data in the steady-state data extraction step. [Effects of the Invention]

[0013] According to the present invention, steady-state data to be used for diagnosis can be extracted efficiently in a short time. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a diagnostic device according to the prior art. [Figure 2] FIG. 2 is a diagram showing an example of time-series data acquired by the measurement unit 1. [Figure 3] 1 is a diagram showing an example of the configuration of a diagnostic device according to a first embodiment of the present invention. [Figure 4] 10A and 10B are diagrams showing examples of time-series data that are measurement values, time intervals that define divided waveforms generated from this time-series data, and curves that show the time change of a stationarity index calculated from these divided waveforms. [Figure 5] FIG. 10 is a diagram showing an example of the configuration of a diagnostic device according to a second embodiment of the present invention. [Figure 6] FIG. 10 is a diagram showing an example of the configuration of a diagnostic device according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] In the present invention, the waveform of time-series data, such as current measured from the equipment to be diagnosed, is divided into multiple waveforms (divided waveforms) having an arbitrary time width (window width). Preferably, the divided waveforms overlap with other divided waveforms for an arbitrary time length. The divided waveforms are converted into a stationarity index (e.g., variance of the fundamental frequency component). In the present invention, an extreme value (e.g., a minimum value) of the stationarity index is found, and the divided waveforms found based on this extreme value are used as steady-state data for the time-series data.

[0016] In the present invention, since a threshold is not used to extract steady-state data from time-series data, adjustment of the threshold is not required. Therefore, even for equipment whose state changes are drastic and whose steady-state duration is short or whose steady-state occurrence frequency is low, steady-state data to be used for diagnosis can be extracted efficiently in a short time.

[0017] Before describing the embodiments of the present invention, an example of a diagnostic device according to the prior art and its problems will be described.

[0018] Fig. 1 is a diagram showing an example of the configuration of a diagnostic device according to the prior art. The diagnostic device shown in Fig. 1 includes a measurement unit 1, a data extraction unit 2, a diagnostic unit 3, and a threshold storage unit 4, and diagnoses the state of a device to be diagnosed.

[0019] The measurement unit 1 measures and acquires time-series data such as current and vibration from equipment. For example, the measurement unit 1 measures the waveform of the current of an electric motor or a power converter (inverter) and acquires this waveform as time-series data.

[0020] The data extraction unit 2 extracts data (waveform) to be used for diagnosis from the time-series data (measured values) measured by the measurement unit 1 based on the thresholds stored in the threshold storage unit 4. The data to be used for diagnosis is steady-state data.

[0021] The diagnosing unit 3 uses the waveform extracted by the data extracting unit 2 to diagnose the state of the device to be diagnosed.

[0022] The threshold value storage unit 4 stores a threshold value used when the data extraction unit 2 extracts data to be used for diagnosis (steady state data) from the measured values. This threshold value is set in advance and stored in the threshold value storage unit 4.

[0023] In a diagnostic device according to the prior art, trial and error is required to set the thresholds stored in the threshold storage unit 4. A method for setting the thresholds in a diagnostic device according to the prior art will be described with reference to FIG.

[0024] FIG. 2 is a diagram showing an example of time-series data acquired by the measurement unit 1, and is a diagram for explaining the problems with diagnostic devices according to the prior art.

[0025] In a conventional diagnostic device, for example, the measurement value reaching a threshold is used as a trigger to start extracting diagnostic data. That is, when the measurement value reaches the threshold, the data extracting unit 2 determines that the measurement value is steady-state data and extracts the steady-state data that is diagnostic data.

[0026] In the example shown in Figure 2, the data extraction unit 2 determines that the measurement value after time t1 is steady-state data because the measurement value reaches the threshold value at time t1. Therefore, the data extraction unit 2 also extracts the measurement value in time interval A as steady-state data. However, as shown in Figure 2, the measurement value in time interval A cannot be considered steady-state data and is not necessarily data suitable for diagnosis. On the other hand, the measurement value in time interval B can be considered steady-state data and is more suitable for diagnosis.

[0027] For this reason, in conventional diagnostic devices, it is necessary to manually adjust the threshold so that measurement values ​​in time interval A are not extracted, but measurement values ​​in time interval B are extracted. Furthermore, for example, if the device to be diagnosed is installed in a vehicle that undergoes acceleration and deceleration, the state of this device changes drastically, and the steady state of measurement values ​​lasts for a short period of time and occurs infrequently. In such cases, if the threshold is not set appropriately, data during acceleration and deceleration may be mistakenly extracted as steady-state data, or the threshold may not be set as a trigger, resulting in no diagnostic data being extracted.

[0028] As described above, conventional diagnostic devices have a problem in extracting steady-state data for use in diagnosis efficiently in a short time.

[0029] A diagnostic device and a diagnostic method according to an embodiment of the present invention will be described below with reference to the drawings. The diagnostic device according to an embodiment of the present invention can be configured with a computer and a sensor that measures time-series data. The diagnostic method according to an embodiment of the present invention is executed by the diagnostic device according to an embodiment of the present invention. The embodiment described below merely shows one example of an embodiment of the present invention, and is not intended to limit the scope of the present invention to the following embodiment.

[0030] In the drawings used in this specification, the same or corresponding components are designated by the same reference numerals, and repeated description of these components may be omitted. [Example]

[0031] 3 is a diagram showing an example of the configuration of a diagnostic device according to a first embodiment of the present invention. The diagnostic device according to this embodiment includes a measurement unit 1, a waveform division unit 5, a stationary index conversion unit 6, a stationary data extraction unit 7, and a diagnosis unit 3, and diagnoses the state of a device to be diagnosed. That is, the diagnostic device according to this embodiment includes the waveform division unit 5, the stationary index conversion unit 6, and the stationary data extraction unit 7, instead of the data extraction unit 2 and threshold storage unit 4 of the conventional diagnostic device shown in FIG. 1.

[0032] The diagnostic device according to this embodiment diagnoses the state of equipment (e.g., an electric motor or a power converter) based on time-series data (e.g., time-series data of current, vibration, pressure, temperature, etc.). In the following embodiment, an example will be mainly described in which the diagnostic device measures the waveform of the current of an electric motor (motor) or a power converter (inverter), acquires this waveform as time-series data, and performs diagnosis. Note that the steady state of the time-series data (waveform) is assumed to have an approximate duration known in advance by measuring the time-series data.

[0033] The measurement unit 1 measures and acquires time-series data from the device to be diagnosed. For example, the measurement unit 1 includes a current sensor and a vibration sensor, and acquires waveforms measured by the current sensor and the vibration sensor, i.e., waveforms of measurement values ​​such as current and vibration, as time-series data.

[0034] The waveform division unit 5 divides the waveform of the time series data acquired by the measurement unit 1 along the time axis, thereby dividing it into multiple waveforms with any time width (window width). The waveform divided by the waveform division unit 5 is called a divided waveform. In other words, the waveform division unit 5 generates multiple divided waveforms. The time interval in which a divided waveform exists is determined by its starting time and time width. The number of divided waveforms generated by the waveform division unit 5 can be determined arbitrarily, and can be determined according to the time width of the divided waveform, for example.

[0035] The time width (window width) of the divided waveforms is a time shorter than the duration of the steady state of the waveform acquired by the measurement unit 1, and is a time of any length that includes multiple measurement times (measurement data). It is preferable that the time widths (window widths) of the multiple divided waveforms all have the same length, but they do not all have to be the same length.

[0036] It is preferable that each divided waveform overlaps with another divided waveform for an arbitrary length of time on the time axis. The length of time (overlap time) that a divided waveform overlaps with another divided waveform can be determined arbitrarily. This overlap time is preferably the same length for all divided waveforms, but does not have to be the same length. When the waveform divider 5 divides the waveform of the time series data into divided waveforms so that the divided waveforms overlap with each other, the diagnostic device according to this embodiment can extract all steady states without omission.

[0037] The longer the time width (window width) of the divided waveforms, the higher the accuracy of the diagnosis performed by the diagnostic unit 3. This is because, for example, when the diagnostic unit 3 performs frequency analysis, a longer data length increases the frequency resolution. However, if the time width of the divided waveforms is too long, the data used for diagnosis may include data other than steady-state data. For example, if the time width of the divided waveforms is set to 20 seconds for time-series data (waveforms) in which the steady state does not last for 10 seconds or more, only divided waveforms including non-steady-state data will be obtained, and it will not be possible to obtain divided waveforms that include only steady-state data. For this reason, it is preferable to determine the time width of the divided waveforms taking into consideration the required diagnostic accuracy and the duration of the steady state of the time-series data (waveforms).

[0038] Regarding the overlap time of the divided waveforms, the longer the overlap time, the higher the possibility that the steady state will be included in the data used for diagnosis. For example, depending on the time of the start point of the divided waveform and the time width of the divided waveform, if the overlap time is short, it may not be possible to reliably include the steady state waveform in the divided waveform. Therefore, the longer the overlap time of the divided waveforms, the higher the possibility of obtaining a divided waveform that includes the steady state. However, if the overlap time is too long, the calculation time for the subsequent process performed by the stationarity index conversion unit 6 (process of converting the divided waveform into a stationarity index) will be long. For this reason, it is preferable to determine the overlap time of the divided waveforms taking into account the calculation resources of the diagnostic device and the duration of the steady state of the time-series data (waveform).

[0039] The stationarity index converter 6 converts each of the divided waveforms generated by the waveform divider 5 into a stationarity index to obtain the stationarity index. The stationarity index is an index for determining whether the time series data (waveform) is in a steady state; for example, the smaller or larger the value, the closer it is to a steady state. Any value can be used as the stationarity index. For example, a value obtained by performing statistical processing on the numerical data representing the divided waveforms (i.e., the data of the measurement values ​​included in the divided waveforms) can be used as the stationarity index.

[0040] In this embodiment, the stationarity index may be, for example, the variance of the fundamental frequency component of a waveform, the variance of the waveform amplitude, or the product of the variance of the fundamental frequency component of a waveform and the variance of the waveform amplitude. In statistics, variance is an index that represents the variation in numerical data, and is calculated by averaging the squared differences between the mean value and each individual value in a group of numerical data. The greater the amount of data that deviates from the mean value, the greater the variance. Therefore, variance can be used as an index for determining the magnitude of variation in the numerical data representing the divided waveform and for determining whether the time series data (waveform) is in a steady state. In other words, the smaller the variance, the closer it can be determined to be to a steady state.

[0041] The steady-state data extraction unit 7 determines the extreme value (minimum or maximum value) of the stationarity index determined by the stationarity index conversion unit 6, and determines divided waveforms including measurement values ​​corresponding to the determined extreme values. If there are multiple divided waveforms determined in this way and the time intervals defining the determined divided waveforms overlap with each other, the steady-state data extraction unit 7 determines the divided waveform including the measurement value corresponding to the smallest minimum value or the largest maximum value. The steady-state data extraction unit 7 regards the divided waveforms determined in this way as steady-state data.

[0042] In this embodiment, the above-mentioned variance is used as the stationarity index, so the steady-state data extraction unit 7 finds the minimum value of the stationarity index and finds divided waveforms including measurement values ​​corresponding to the found minimum values. If there are multiple divided waveforms found in this way and the time intervals defining the found divided waveforms overlap, the steady-state data extraction unit 7 finds the divided waveform including the measurement value corresponding to the smallest minimum value. In this embodiment, the steady-state data extraction unit 7 uses the divided waveforms found in this way as steady-state data.

[0043] In the diagnostic device according to this embodiment, the steady-state data extraction unit 7 finds the extreme values ​​(minimum or maximum values) of the stationarity index in this way, so that steady-state data to be used for diagnosis can be extracted efficiently in a short time. In the diagnostic device according to this embodiment, a threshold is not used to extract steady-state data as in the prior art, and adjustment of the threshold is not required, so steady-state data can be easily obtained.

[0044] An example of a method in which the steady-state data extracting unit 7 obtains divided waveforms, which are steady-state data, will be described in detail with reference to FIG.

[0045] 4 is a diagram showing an example of time series data 20, which is a measurement value, a time interval 21 that defines divided waveforms generated from this time series data 20, and a curve 22 that shows the time change in the stationarity index calculated from this divided waveform. As an example, FIG. 4 shows an example in which the waveform dividing unit 5 divides the time series data into 11 divided waveforms.

[0046] It is assumed that the stationarity index conversion unit 6 has already calculated the stationarity index from the divided waveforms for each time interval 21 that defines the plurality of divided waveforms generated by the waveform dividing unit 5.

[0047] The steady-state data extracting unit 7 obtains the divided waveforms, which are steady-state data, as follows. In this embodiment, as described above, variance is used as the index of stationarity. Therefore, the steady-state data extracting unit 7 obtains the minimum value of variance as the extreme value of the index of stationarity.

[0048] The steady-state data extraction unit 7 finds a minimum value 23 of the stationarity index for each time interval 21 that defines the divided waveform in a curve 22 that shows the time change of the stationarity index. It is not necessary to find a minimum value for a time interval 21 in which no minimum value is found. As an example, three minimum values ​​23a, 23b, and 23c are shown in FIG. 4.

[0049] Next, the steady-state data extracting unit 7 obtains a divided waveform (extreme value divided waveform) including a measurement value corresponding to the obtained minimum value 23. In the example shown in Fig. 4, the steady-state data extracting unit 7 obtains a divided waveform defined by time intervals 21a, 21b, and 21c as the extreme value divided waveform.

[0050] Next, the steady-state data extracting unit 7 obtains steady-state data from the extreme value divided waveform.

[0051] If there is only one extreme value division waveform, the steady-state data extraction unit 7 takes this extreme value division waveform as steady-state data.

[0052] When there are multiple extreme value division waveforms, and there are any extreme value division waveforms that do not overlap with other extreme value division waveforms on the time axis, the steady-state data extraction unit 7 treats these extreme value division waveforms as steady-state data.

[0053] When there are multiple extreme value division waveforms and an extreme value division waveform overlaps with another extreme value division waveform on the time axis, the steady-state data extraction unit 7 determines, among the overlapping extreme value division waveforms, the extreme value division waveform that includes the measurement value corresponding to the smallest minimum value 23 as steady-state data. Note that, when the extreme value of the index of stationarity is a maximum value, the extreme value division waveform that includes the measurement value corresponding to the largest maximum value is determined to be steady-state data.

[0054] If a divided waveform overlaps with another divided waveform, the same measurement value will be included in multiple divided waveforms. In such a case, one measurement value may be used in multiple calculations, which may have a negative impact on the subsequent diagnosis of the device by the diagnostic unit 3. For this reason, if a divided waveform overlaps with another divided waveform, it is preferable to use the divided waveform that includes the measurement value corresponding to the smallest minimum value 23 as the steady-state data.

[0055] In the example shown in FIG. 4, there are three extreme value divided waveforms (divided waveforms including measurement values ​​corresponding to minimum values ​​23a, 23b, and 23c). The extreme value divided waveform including the measurement value corresponding to minimum value 23a is a divided waveform determined in time interval 21a. The extreme value divided waveform including the measurement value corresponding to minimum value 23b is a divided waveform determined in time interval 21b. The extreme value divided waveform including the measurement value corresponding to minimum value 23c is a divided waveform determined in time interval 21c. Minimum value 23b is smaller than minimum value 23c.

[0056] The steady-state data extraction unit 7 determines that the extreme value divided waveform (divided waveform determined in the time interval 21a) including the measurement value corresponding to the minimum value 23a does not overlap with other extreme value divided waveforms, and therefore determines the divided waveform determined in the time interval 21a as steady-state data 24a. On the other hand, the extreme value divided waveform (divided waveform determined in the time interval 21b) including the measurement value corresponding to the minimum value 23b and the extreme value divided waveform (divided waveform determined in the time interval 21c) including the measurement value corresponding to the minimum value 23c overlap with each other. In this case, the minimum value 23b is smaller than the minimum value 23c, and therefore the divided waveform determined in the time interval 21b is determined as steady-state data 24b.

[0057] Returning to the explanation of Figure 3.

[0058] The diagnostic unit 3 diagnoses the state of the equipment to be diagnosed by any known method using the divided waveforms obtained as steady-state data by the steady-state data extraction unit 7. For example, the diagnostic unit 3 performs frequency analysis on the steady-state data to diagnose the state of equipment such as an electric motor or a power converter. When the steady-state data extraction unit 7 extracts multiple pieces of steady-state data (divided waveforms), the diagnostic unit 3 performs diagnosis on these multiple pieces of steady-state data.

[0059] In this embodiment, the steady-state data extraction unit 7 determines the minimum value 23 of the stationarity index and sets the divided waveform including the measurement value corresponding to the determined minimum value 23 as steady-state data, so that it is possible to extract the waveform in a time interval where the change in the measurement value is small as steady-state data. In this way, in this embodiment, data that is highly stationary and suitable for diagnosis, i.e., steady-state data to be used for diagnosis, can be extracted efficiently in a short time.

[0060] In this embodiment, the waveform of the time-series data of the measurement values ​​is divided by an arbitrary time width (window width), and the variance, which is an index of stationarity, is calculated for the resulting waveform (divided waveform). The waveform for the time interval containing the minimum value of the variance is extracted as steady-state data. In this case, simply extracting the waveform for the time interval containing the minimum value may result in the same measurement value being included in the extracted multiple waveforms. This may have an adverse effect on the diagnosis of the device by the diagnosis unit 3. For example, when calculating the moving average or mean value of the diagnosis results, the waveform for a specific time interval may have a significant influence, which may lead to an erroneous interpretation of the diagnosis.

[0061] Therefore, in this embodiment, the steady-state data extraction unit 7 takes into consideration the time width (window width) of the divided waveforms, and among the overlapping extreme value divided waveforms, treats the extreme value divided waveform that includes the measurement value corresponding to the smallest minimum value 23 as steady-state data, thereby preventing the extracted extreme value divided waveforms from including the same measurement value. In this way, this embodiment can avoid being heavily influenced by the waveform in a specific time interval, and can perform more accurate diagnosis. [Example]

[0062] A diagnostic device according to a second embodiment of the present invention will be described. In this embodiment, an example will be described in which the devices to be diagnosed are a power supply, a power converter, a rotating machine (e.g., a motor), and a load device, the time-series data measured and acquired by the measurement unit 1 is the waveform of the current of the rotating machine, and the data used for diagnosis is saved in a data logger. Below, the diagnostic device according to this embodiment will be described, mainly focusing on the differences from the diagnostic device according to the first embodiment.

[0063] FIG. 5 is a diagram showing an example of the configuration of a diagnostic device according to this embodiment.

[0064] The measurement unit 1 includes a current sensor, and measures the current flowing from the power converter 9 to the rotating machine 10 using the current sensor. The power converter 9 is supplied with power from a power source 8. The rotating machine 10 is electrically connected to the power converter 9. The load device 11 is mechanically connected to the rotating machine 10.

[0065] The waveform dividing unit 5, the stationary index converting unit 6, and the stationary data extracting unit 7 are provided in the data logger 13.

[0066] The data logger 13 records the current data measured by the measurement unit 1. The data logger 13 does not output all of the recorded data to the diagnosis unit 3, but can output only the steady-state data extracted by the steady-state data extraction unit 7 to the diagnosis unit 3 for diagnosis.

[0067] The data logger 13 temporarily stores the time series data continuously measured by the measurement unit 1 in a volatile memory (e.g., RAM) with a high writing speed. Then, the waveform dividing unit 5 divides the waveform of this time series data into divided waveforms, the stationary index converting unit 6 converts the divided waveforms into stationary indexes, and the stationary data extracting unit 7 finds the extreme values ​​of the stationary indexes to extract stationary state data.

[0068] The data logger 13 outputs only the steady-state data extracted by the steady-state data extraction unit 7 to the diagnosis unit 3 by saving it in a storage device or transferring it via a network. Therefore, the data logger 13 can output the steady-state data required for diagnosis to the diagnosis unit 3 even if the storage capacity of the storage device is small or the network bandwidth capacity is small.

[0069] In this embodiment, the equipment to be diagnosed can be at least one of the power supply 8, the power converter 9, the rotating machine 10, and the load device 11. A technique called MCSA (Motor Current Signature Analysis) has shown that signs of deterioration appear at specific frequencies in the currents of the rotating machine 10 and the load device 11. Furthermore, fluctuations in the amplitude and frequency of the current and vibrations of the power supply 8 and the power converter 9 appear as pulsations in the current flowing through the rotating machine 10 (for example, a motor). [Example]

[0070] A diagnostic device according to a third embodiment of the present invention will be described. When the steady-state data extraction unit 7 extracts a plurality of steady-state data (divided waveforms), the diagnostic devices according to the first and second embodiments use all of the plurality of steady-state data for diagnosis in the diagnostic unit 3. In this embodiment, an example will be described in which data to be used for diagnosis in the diagnostic unit 3 is selected from the plurality of steady-state data. Below, the diagnostic device according to this embodiment will be described, mainly focusing on the differences from the diagnostic device according to the first embodiment.

[0071] 6 is a diagram showing an example of the configuration of a diagnostic device according to this embodiment. The diagnostic device according to this embodiment further comprises a selection threshold storage unit 15 and a data selection unit 14 in addition to the components of the diagnostic device according to embodiment 1 (FIG. 3).

[0072] The selection threshold storage unit 15 stores a threshold for selecting data to be used for diagnosis in the diagnosis unit 3. This threshold can be determined arbitrarily in advance.

[0073] The data selection unit 14 uses a threshold stored in the selection threshold storage unit 15 to select data to be used for diagnosis in the diagnosis unit 3 from the multiple steady-state data (divided waveforms) extracted by the steady-state data extraction unit 7.

[0074] In the diagnostic device according to this embodiment, the data selection unit 14 selects data to be used for diagnosis by the diagnostic unit 3 before the diagnostic unit 3 performs a diagnosis. For example, if the equipment to be diagnosed is equipment that operates under multiple loads or multiple rotation speed conditions, the measurement values ​​(waveforms of time-series data) of these equipment will have multiple steady states depending on the operating conditions. The data selection unit 14 selects data to be used for diagnosis from the steady-state data extracted by the steady-state data extraction unit 7 using a threshold value that matches the operating conditions of the equipment to be diagnosed.

[0075] This threshold value can be, for example, a threshold value for one or both of the amplitude and fundamental frequency of the time-series data measured and acquired by the measurement unit 1. For example, if the measured time-series data is data about a motor current, the effective value (RMS) or fundamental frequency of the motor current can be used as the threshold value. For example, the data selection unit 14 selects data in which the effective value of the motor current is in the range of 100±1 A and data in which the fundamental frequency of the motor current is in the range of 50±1 Hz from the steady-state data extracted by the steady-state data extraction unit 7, and can use these data for diagnosis in the diagnosis unit 3. The selection threshold value storage unit 15 stores the effective value of 100±1 A and the fundamental frequency of 50±1 Hz for the motor current as threshold values.

[0076] In this way, the diagnostic device according to this embodiment can efficiently extract steady-state data to be used for diagnosis in a short time, even for equipment that has multiple operating states and multiple steady-state measurement values, and can perform accurate diagnosis.

[0077] It should be noted that the present invention is not limited to the above-described embodiments, and various modifications are possible. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to embodiments that include all of the configurations described. It is also possible to replace part of the configuration of one embodiment with the configuration of another embodiment. It is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to delete part of the configuration of each embodiment, or to add or replace other configurations. [Explanation of symbols]

[0078] 1...measurement unit, 2...data extraction unit, 3...diagnosis unit, 4...threshold value storage unit, 5...waveform division unit, 6...steadiness index conversion unit, 7...steadiness data extraction unit, 8...power supply, 9...power converter, 10...rotating machine, 11...load device, 13...data logger, 14...data selection unit, 15...selection threshold value storage unit, 20...time series data, 21, 21a, 21b, 21c...time interval, 22...curve showing time change of stationarity index, 23, 23a, 23b, 23c...local minimum value, 24a, 24b...steady state data.

Claims

1. a measurement unit that measures and acquires time-series data from the device to be diagnosed; a waveform dividing unit that divides the waveform of the time series data into a plurality of waveforms along a time axis to generate a plurality of divided waveforms; a stationarity index conversion unit that calculates a stationarity index, which is an index for determining whether the time-series data is in a stationary state, for each of the plurality of divided waveforms generated by the waveform dividing unit; a steady-state data extraction unit that calculates extreme values ​​of the steady-state index calculated by the steady-state index conversion unit, and calculates the divided waveforms including measurement values ​​corresponding to the extreme values ​​as steady-state data; a diagnosis unit that diagnoses the state of the device using the divided waveforms obtained by the steady-state data extraction unit as the steady-state data; Equipped with A diagnostic device characterized by:

2. the waveform dividing unit generates the plurality of divided waveforms so that the divided waveforms overlap with each other on the time axis. The diagnostic device of claim 1 .

3. When there are a plurality of extreme value divided waveforms, which are divided waveforms including measurement values ​​corresponding to the extreme values, and when there is an extreme value divided waveform that overlaps with another extreme value divided waveform on the time axis, the steady state data extraction unit selects an extreme value divided waveform that includes a measurement value corresponding to the minimum or maximum extreme value among the overlapping extreme value divided waveforms as the steady state data. The diagnostic device of claim 1 .

4. the index of stationarity is a variance of the fundamental frequency of the time series data, a variance of the amplitude of the time series data, or a product of the variance of the fundamental frequency of the time series data and the variance of the amplitude of the time series data; The diagnostic device of claim 1 .

5. the device is an electric motor or a power converter; the time-series data is a waveform of a measurement value of a current or a vibration, the diagnosing unit performs frequency analysis on the steady-state data to diagnose the state of the device. The diagnostic device of claim 1 .

6. a selection threshold storage unit storing a threshold for selecting data to be used for diagnosis in the diagnosis unit; a data selection unit that selects data to be used for diagnosis by the diagnosis unit from the divided waveforms obtained as the steady-state data by the steady-state data extraction unit using the threshold value; Equipped with The diagnostic device of claim 1 .

7. the threshold is one or both of a value for the amplitude of the time series data and a value for the fundamental frequency of the time series data; The diagnostic device of claim 6.

8. a measurement step of measuring and acquiring time-series data from the device to be diagnosed; a waveform dividing step of dividing the waveform of the time series data into a plurality of waveforms along a time axis to generate a plurality of divided waveforms; a stationarity index conversion step of calculating a stationarity index, which is an index for determining whether the time-series data is in a stationary state, for each of the plurality of divided waveforms generated in the waveform dividing step; a steady-state data extraction step of determining extreme values ​​of the stationarity index determined in the stationarity index conversion step, and determining the divided waveforms including measurement values ​​corresponding to the extreme values ​​as steady-state data; a diagnosis step of diagnosing the state of the device using the divided waveforms obtained as the steady-state data in the steady-state data extraction step; having A diagnostic method characterized by:

9. In the waveform dividing step, a plurality of divided waveforms are generated so that each divided waveform overlaps with another divided waveform on the time axis. The diagnostic method according to claim 8.

10. In the steady-state data extraction step, when there are a plurality of extreme value divided waveforms, which are divided waveforms including measurement values ​​corresponding to the extreme values, and when there is an extreme value divided waveform that overlaps with another extreme value divided waveform on the time axis, the extreme value divided waveform that includes a measurement value corresponding to the minimum or maximum extreme value among the mutually overlapping extreme value divided waveforms is set as the steady-state data. The diagnostic method according to claim 8.

11. the index of stationarity is a variance of the fundamental frequency of the time series data, a variance of the amplitude of the time series data, or a product of the variance of the fundamental frequency of the time series data and the variance of the amplitude of the time series data; The diagnostic method according to claim 8.

12. the device is an electric motor or a power converter; the time-series data is a waveform of a measurement value of a current or a vibration, In the diagnosing step, a frequency analysis is performed on the steady-state data to diagnose the state of the device. The diagnostic method according to claim 8.

13. a data selection step of selecting data to be used for diagnosis in the diagnosis step from the divided waveforms obtained as the steady-state data in the steady-state data extraction step, using a threshold value for selecting data to be used for diagnosis in the diagnosis step; The diagnostic method according to claim 8.

14. the threshold is one or both of a value for the amplitude of the time series data and a value for the fundamental frequency of the time series data; The diagnostic method according to claim 13.

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