Abnormality diagnosis device and method of rotating electrical machines

Abstract

To provide an abnormality diagnosis device and a method of rotating electrical machines capable of accurately determining whether there is an abnormality in the rotating electrical machine.SOLUTION: A motor abnormality monitoring device is an abnormality monitoring device for a rotating electric machine that includes an abnormality determination unit for determining whether an abnormality exists in the rotating electrical machine based on current time series data representing time-dependent changes in AC in the rotating electrical machine. The motor abnormality monitoring device includes: a waveform determination unit that determines whether or not the AC waveform shown in the current time series data includes a specific waveform indicating the operating state of a rotating electrical machine that is not subject to abnormality monitoring; and a data processing unit that is configured so as to, when determined that a specific waveform is included, execute a series of data processing to exclude the time series data of the non-target AC that includes the AC corresponding to the specific waveform from the target of the abnormality determination by the abnormality determination unit.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to an abnormality diagnosis device and an abnormality diagnosis method for a rotating electrical machine.

Background Art

[0002] Patent Document 1 discloses a diagnosis device for a motor. The diagnosis device includes a data acquisition unit that acquires target data related to a current value between a power conversion device and a motor, a detection unit that detects a peak value of the current in the time-series waveform of the target data, and a diagnosis unit that diagnoses an abnormality of the motor based on the detected peak value.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the above diagnosis device, the current value may be measured even during a period when the motor is in an unfavorable operating state for abnormality diagnosis, and the current value may be reflected in the target data. As a more specific example, during a period when the motor is in a starting operation state or a stopping operation state, a current value of a magnitude not suitable for abnormality diagnosis may be measured. When the diagnosis unit performs diagnosis based on the target data in which such a current value is reflected, accurate abnormality diagnosis may not be possible.

[0005] An object of the present disclosure is to provide an abnormality diagnosis device and an abnormality diagnosis method for a rotating electrical machine that can accurately determine whether there is an abnormality in the rotating electrical machine.

Means for Solving the Problems

[0006] An abnormality monitoring device for a rotating electrical machine according to at least one embodiment of the present disclosure is An abnormal monitoring device for a rotating electrical machine, comprising an abnormality determination unit for determining whether there is an abnormality in the rotating electrical machine based on current time-series data indicating a change over time of an alternating current in the rotating electrical machine, a waveform determination unit for determining whether a specific waveform indicating an operating state of the rotating electrical machine, which is not an object of abnormal monitoring, is included in the waveform of the alternating current indicated by the current time-series data, and a data processing unit for executing data processing to exclude time-series data of non-target alternating current including the alternating current corresponding to the specific waveform from being an object of abnormality determination by the abnormality determination unit when it is determined that the specific waveform is included.

[0007] An abnormal monitoring method for a rotating electrical machine according to at least one embodiment of the present disclosure is an abnormal monitoring method for a rotating electrical machine, comprising an abnormality determination step for determining whether there is an abnormality in the rotating electrical machine based on current time-series data indicating a change over time of an alternating current in the rotating electrical machine, a waveform determination step for determining whether a specific waveform indicating an operating state of the rotating electrical machine, which is not an object of abnormal monitoring, is included in the waveform of the alternating current indicated by the current time-series data, and a data processing step for executing data processing to exclude time-series data of non-target alternating current including the alternating current including the specific waveform from being an object of abnormality determination in the abnormality determination step when it is determined that the specific waveform is included.

Advantages of the Invention

[0008] According to the present disclosure, it is possible to provide an abnormal diagnosis device and an abnormal diagnosis method for a rotating electrical machine that can accurately determine whether there is an abnormality in the rotating electrical machine.

Brief Description of the Drawings

[0009]

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MODE FOR CARRYING OUT THE INVENTION

[0010] Hereinafter, some embodiments of the present disclosure will be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of the components described as embodiments or shown in the drawings are not intended to limit the scope of the present disclosure thereto, but are merely illustrative examples. For example, expressions indicating relative or absolute arrangements such as "in a certain direction", "along a certain direction", "parallel", "orthogonal", "center", "concentric", or "coaxial" not only precisely represent such arrangements, but also represent states with tolerances or relatively displaced by angles or distances to the extent that the same function can be obtained. For example, expressions indicating that things are in an equal state such as "identical", "equal", and "homogeneous" not only precisely represent an equal state, but also represent states with tolerances or differences existing to the extent that the same function can be obtained. For example, expressions representing shapes such as a square shape or a cylindrical shape not only represent the shapes such as a square shape or a cylindrical shape in a geometrically precise sense, but also represent shapes including uneven portions, chamfered portions, etc. within the range where the same effect can be obtained. On the other hand, expressions such as "comprising", "including", or "having" one component are not exclusive expressions excluding the existence of other components. Note that the same reference numerals may be given to similar configurations and the description may be omitted.

[0011] <Outline of the Abnormality Monitoring System 10> FIG. 1 is a schematic diagram of an abnormality monitoring system 10 according to an embodiment of the present disclosure. The abnormality monitoring system 10 includes a motor 7, a power supply device 2 for supplying alternating current power to the motor 7, a mechanical device 8 driven by the motor 7, a measuring device 6 for measuring the power supply current of the motor 7, an A / D converter 3 for converting a measured value as an analog value of the power supply current output from the measuring device 6 into a digital value, and a motor abnormality monitoring device 1 electrically connected to the A / D converter 3. The motor 7 is an example of the "rotating electrical machine" of the present invention, and the motor abnormality monitoring device 1 is an example of the "abnormality monitoring device for a rotating electrical machine" of the present invention.

[0012] The motor 7 is a three-phase AC motor as an example (however, the present disclosure is not limited thereto, and the motor 7 may be a DC motor). The power supply equipment 2 includes a three-phase AC power supply as an example (however, the present disclosure is not limited thereto, and the power supply equipment 2 may be an inverter power supply). The mechanical device 8 may be any movable device as long as it is a device driven by the motor 7. Although it is merely an example, the mechanical device 8 may be a movable device incorporated in a gas turbine system, and more specifically, it may be a blower.

[0013] The measuring device 6 is configured to measure the power supply current of the motor 7. For example, the current value flowing through the electric wire 4 connected to the power supply equipment 2 and the motor 7 may be measured by the measuring device 6. The power supply current may be the exciting current in any coil of the motor 7. Hereinafter, the current to be measured by the measuring device 6 may be simply referred to as the "power supply current". The power supply current is an example of the "alternating current" of the present invention. The A / D converter 3 is configured to convert the current value continuously measured by the measuring device 6 into a digital value. The measured time-series data Dm generated by the A / D converter 3 indicates the change over time of the measured power supply current. The measured time-series data Dm is sent to the motor abnormality monitoring device 1.

[0014] The motor abnormality monitoring device 1 is constituted by a computer and includes a processor, a memory (storage medium), and an external communication interface. The processor is a CPU, GPU, MPU, DSP, or a combination thereof. The processor according to other embodiments may be realized by an integrated circuit such as a PLD, ASIC, FPGA, or MCU. The memory is configured to store various data temporarily or non-temporarily, and is realized by, for example, at least one of RAM, ROM, or flash memory. According to the program instructions loaded into the memory, the processor executes various control processes.

[0015] <Overview of the motor abnormality monitoring device 1> FIG. 2 is a schematic diagram of a motor abnormality monitoring device 1 according to an embodiment of the present disclosure. The motor abnormality monitoring device 1 includes a time-series data generation unit 15 for generating current time-series data Da based on measurement time-series data Dm sent from an A / D converter 3, and an abnormality determination unit 28 for determining whether there is an abnormality in the motor 7 based on the current time-series data Da.

[0016] FIG. 3 is a schematic diagram of current time-series data Da generated by the time-series data generation unit 15 according to an embodiment of the present disclosure. The current time-series data Da includes a plurality of divided time-series data Dv indicating the temporal change of the power supply current in different time zones. The time width (dimension L) in each of the plurality of divided time-series data Dv is constant, and as an example, it is 5 seconds or more and 20 seconds or less. The time-series data generation unit 15 generates the current time-series data Da from the measurement time-series data Dm by executing data processing for cutting out the measurement time-series data Dm generated by the A / D converter 3 at predetermined time widths.

[0017] The abnormality determination unit 28 illustrated in FIG. 2 generates spectrum data by performing fast Fourier transform processing on each of the plurality of divided time-series data Dv included in the current time-series data Da as an example, and determines whether there is an abnormality in the motor 7 based on the spectrum data. As a more specific example, a plurality of spectrum data corresponding to the plurality of divided time-series data Dv are generated. When the spectrum intensity corresponding to a specific frequency deviates from the allowable range in any of the spectrum data, the abnormality determination unit 28 determines that there is an abnormality in the motor 7. Note that the present disclosure is not limited to the Fourier transform processing being performed on the current time-series data Da. As another example, the abnormality determination unit 28 may directly determine whether there is an abnormality in the motor 7 from the current time-series data Da.

[0018] The inventors of the present application have found that when the plurality of divided time-series data Dv includes data that is not suitable for abnormality determination, the abnormality determination by the abnormality determination unit 28 may not be performed accurately. For example, due to the rotational resistance of the mechanical device 8 or the like, the motor load applied to the motor 7 may become temporarily or instantaneously unstable. Such a phenomenon can be confirmed while the motor 7 is in the starting operation state, the stopping operation state, or the rated operation state under limited conditions. When the current value measured by the measurement device 6 fluctuates greatly temporarily or instantaneously along with the fluctuation of the motor load, the above-described spectral intensity may exceed the allowable range. In this case, an erroneous determination phenomenon occurs in which the abnormality determination unit 28 determines that there is an abnormality in the motor 7 although there is actually no abnormality in the motor 7.

[0019] Therefore, in the embodiment illustrated in FIG. 2, the motor abnormality monitoring device 1 includes a waveform determination unit 30 and a data processing unit 29 so that data that is not suitable for abnormality determination and may be included in the plurality of divided time-series data Dv is not used as a basis for determination by the abnormality determination unit 28.

[0020] The waveform determination unit 30 is configured to determine whether a specific waveform 90 is included in the waveform 9 of the power supply current indicated by the current time-series data Da (see FIG. 4). The specific waveform 90 indicates an operating state that is not subject to abnormality monitoring during the operating state of the motor 7. Which operating state is excluded is defined in advance by a program stored in the memory of the motor abnormality monitoring device 1. The waveform determination unit 30 determines whether a specific waveform 90 is included in the waveform 9 of the power supply current for each of the plurality of divided time-series data Dv. For example, as shown in FIG. 4, when the absolute value of the power supply current is extremely low over a certain period of time, the current waveform drawn by the power supply current is determined to be the specific waveform 90. Note that the specific waveform 90 shown in FIG. 4 is merely an example of the present disclosure.

[0021] Hereinafter, the power supply current that draws the specific waveform 90 may be referred to as a "non-target power supply current". The waveform 9 of the non-target power supply current may include a part of the normal waveform 9 in addition to the specific waveform 90. The non-target power supply current is an example of the "non-target alternating current" of the present invention.

[0022] The waveform determination unit 30 illustrated in FIG. 2 treats the divided time-series data Dv including the non-target power supply current among the plurality of divided time-series data Dv as the time-series data D1, and sends data for identifying the time-series data D1 to the data processing unit 29. The data for identification may be the time-series data D1 itself.

[0023] The data processing unit 29 is configured to execute data processing on the current time-series data Da such that the time-series data D1 indicating the non-target power supply current is excluded from the target of the abnormality determination by the abnormality determination unit 28. In one embodiment of the present disclosure, the time-series data D1 is deleted and not input to the abnormality determination unit 28. Then, the remaining plurality of divided time-series data Dv included in the current time-series data Da are input to the abnormality determination unit 28.

[0024] According to the above configuration, when the specific waveform 90 indicating the operating state excluded from the abnormality monitoring among the operating states of the motor 7 is included in the waveform 9 of the alternating current indicated by the current time-series data Da, the time-series data D1 of the non-target power supply current depicting the specific waveform 90 is treated as excluded from the target of the abnormality determination by the abnormality determination unit 28. Thereby, the abnormality determination unit 28 can determine the presence or absence of an abnormality in the motor 7 based on the current time-series data Da suitable for the abnormality determination. Therefore, the motor abnormality monitoring device 1 that can accurately determine whether there is an abnormality in the motor 7 is realized.

[0025] Hereinafter, three detailed examples of the waveform determination unit 30 that detects the specific waveform 90 are illustrated. In each detailed example, the operating state of the motor 7 indicated by the specific waveform 90 is explained.

[0026] <Details of the waveform determination unit 30 (first illustration)> FIG. 5 is a schematic diagram of the waveform determination unit 30A (30) according to the first exemplary embodiment. In the first exemplary embodiment, the specific waveform 90 includes a burst waveform 98 indicating the starting operation state of the motor 7 or the operation state of the motor 7 in which a temporary load fluctuation (for example, an increase in load) has occurred (see FIG. 6). The inrush current that instantaneously takes a very high absolute value immediately after the motor 7 that was in the stopped state starts operating, or the first current that instantaneously takes a very high absolute value due to a temporary load fluctuation, corresponds to the burst current. In the first exemplary embodiment, the current waveform depicted by the burst current is regarded as the burst waveform 98.

[0027] Referring to FIG. 5, a configuration for detecting the burst waveform 98 will be described. The waveform determination unit 30A according to the first exemplary embodiment includes a burst waveform determination unit 38 configured to determine whether the burst waveform 98 is included in the waveform 9 of the power supply current. The burst waveform determination unit 38 determines that the burst waveform 98 is included in the waveform 9 when any of the power supply currents of the plurality of divided time-series data Dv includes a burst current whose absolute value exceeds a first specified threshold value. The first specified threshold value is a numerical value used for determining the burst current. Although it is merely an example, the first specified threshold value is a numerical value that is 90% or more of the upper limit value of the current value that the measuring device 6 can measure. In FIG. 6, the first specified threshold value is also set below the t-axis, but is not shown for the sake of simplifying the drawing.

[0028] The burst waveform determination unit 38 treats the divided time-series data Dv in which the burst waveform 98 is determined to be included as the time-series data D1 of the non-target power supply current. The time-series data D1 is deleted by the data processing unit 29 (see FIG. 2).

[0029] According to the inventor's findings, the inrush current that can be measured while the motor 7 is in the starting operation state, or the first current that changes rapidly due to a load fluctuation or the like of the motor 7, has a very high absolute value. Therefore, the power supply current including such a burst current may hinder accurate abnormality determination. In this regard, according to the above configuration, the time-series data D1 of the non-target power supply current depicting the burst waveform 98 is treated as being outside the scope of abnormality determination. Thereby, the abnormality determination unit 28 can accurately determine whether there is an abnormality in the motor 7.

[0030] In the first exemplary case, further, the specific waveform 90 includes a start / stop waveform 97 indicating either the starting operation state or the stopping operation state of the motor 7 (see FIGS. 6 and 7). For example, as shown in FIG. 6, when the motor 7 is started, a weak current that draws the start / stop waveform 97 occurs before the occurrence of the inrush current. The weak current is a power supply current whose absolute value becomes equal to or less than the second specified threshold value over a predetermined time period. As shown in FIG. 7, the start / stop waveform 97 can also occur in the stopping operation state of the motor 7. When the motor 7 changes from the rated operation state to the stopping operation state, the motor speed gradually decreases from the rated speed, and a weak current that draws the start / stop waveform 97 is generated. Note that, in FIGS. 6 and 7, the second specified threshold value can also be set below the t-axis, but is not shown for the sake of simplifying the drawing.

[0031] Referring to FIG. 5, a configuration for detecting the start / stop waveform 97 will be described. The waveform determination unit 30A further includes a start / stop waveform determination unit 37 configured to determine whether a weak current is included in the power supply current. The start / stop waveform determination unit 37 is configured to determine that the start / stop waveform 97 is included in the waveform 9 when a weak current whose absolute value is equal to or less than the second specified threshold value is included in any of the power supply currents of the plurality of divided time-series data Dv.

[0032] The second specified threshold value is a numerical value used to determine the presence or absence of a weak current. Although it is merely an example, the second specified threshold value is a numerical value that is 30% or less, and more specifically 25% or less, of the upper limit value of the rated current value of the motor 7.

[0033] The start / stop waveform determination unit 37 treats the divided time-series data Dv in which it is determined that the start / stop waveform 97 is included as the time-series data D1 of the non-target power supply current. The time-series data D1 is deleted by the data processing unit 29 (see FIG. 2).

[0034] According to the inventor's findings, while the motor 7 is in the starting operation state or the stopping operation state, the weak current has a very small value over a predetermined time, so the power supply current including the weak current may interfere with accurate abnormality determination. In this regard, according to the above configuration, the time-series data D1 of the non-target power supply current including the start-stop waveform 97 is treated as out of the scope of abnormality determination. Thereby, the abnormality determination unit 28 can accurately determine whether there is an abnormality in the motor 7.

[0035] <Details of the waveform determination unit 30 (second example)> FIG. 8 is a schematic diagram of the waveform determination unit 30B (30) according to the second example. In the second example, the specific waveform 90 includes the load reduction waveform 96 (see FIG. 9). The load reduction waveform 96 indicates the operating state of the motor 7 when the motor load instantaneously decreases during rated operation. In addition, the load reduction waveform 96 also occurs when the operating state of the motor 7 becomes instantaneously unstable due to insufficient power capacity of the power supply facility 2 (see FIG. 1). Taking the case where the motor load instantaneously decreases during the operation of the motor 7 (which may be during rated operation) as an example, the torque required to drive the motor 7 instantaneously decreases. In this case, the maximum absolute value of the power supply current measured by the measuring device 6 instantaneously falls below the first reference value as the threshold value. Hereinafter, such a power supply current may be referred to as a "low current".

[0036] As shown in FIG. 9, the low current that depicts the load reduction waveform 96 can occur instantaneously in the basic time zone corresponding to one cycle of the power supply current (that is, in the basic waveform 95 corresponding to one cycle of the waveform 9 of the power supply current). In this example, a plurality of basic waveforms 95 are specified from the waveform 9 of the current time-series data Da. Then, for each of the plurality of basic waveforms 95, it is determined whether the power supply current includes a low current. If it is determined that a low current is included, it is determined that the basic waveform 95 includes the load reduction waveform 96. Note that the basic time zone (dimension M0) corresponding to one cycle of the power supply current may vary slightly among the plurality of detected basic waveforms 95.

[0037] Here, the first reference value, which is a threshold for detecting a low current, is a value obtained by multiplying the peak value corresponding to 1.41 times the effective value of the power supply current by a first coefficient smaller than 1. Although it is just an example, the first coefficient is a value of 0.7 or more and less than 1.0, and more specifically, a value of 0.8 or more and 0.9 or less. The peak value of the power supply current may differ among the plurality of divided time-series data Dv. Therefore, the first reference value is specified in association with each of the plurality of divided time-series data Dv. Although the first reference value is also set below the t-axis in FIG. 9, illustration is omitted for the sake of simplifying the drawing.

[0038] Referring to FIG. 8, a configuration for detecting the load reduction waveform 96 will be described. The waveform determination unit 30B according to the second example includes a first acquisition unit 21 for acquiring the first reference value for each of the plurality of divided time-series data Dv included in the current time-series data Da. The first acquisition unit 21 acquires the first reference value by multiplying the peak value of the power supply current by the first coefficient for each of the divided time-series data Dv. The first acquisition unit 21 also executes data processing for associating the plurality of first reference values with the plurality of divided time-series data Dv, respectively. The result of the data processing is output to a load reduction waveform determination unit 36 (described later), which is a component of the waveform determination unit 30B.

[0039] The waveform determination unit 30B further includes a specifying unit 23. The specifying unit 23 is configured to specify a plurality of basic waveforms 95, each of which is regarded as one cycle of the power supply current, in the waveform 9 of the power supply current indicated by each of the plurality of divided time-series data Dv.

[0040] An example of a method for specifying the basic waveform 95 is as follows. As shown in FIG. 10, the intersections of the current waveform and the time axis include two types of points (zero-crossing points). The first point is the rising point P1 where the current value switches from a negative value to a positive value (including 0), and the second point is the falling point P2 where the current value switches from a positive value (including 0) to a negative value. The specifying unit 23 specifies the basic waveform 95 by detecting the rising point P1 and the falling point P2 that are alternately arranged on the time axis. However, the method for specifying the basic waveform 95 is not limited to this. Instead of the falling point P1 and the rising point P2, it is also possible to specify the basic waveform 95 by detecting the maximum value and the minimum value of the power supply current. Regardless of which embodiment is adopted, the specifying result of the specifying unit 23 is output to the load reduction waveform determination unit 36 (described later), which is a component of the waveform determination unit 30B.

[0041] The load reduction waveform determination unit 36 illustrated in FIG. 8 is configured to determine whether the load reduction waveform 96 is included in the waveform 9 of the power supply current based on the first reference value and the basic waveform 95. More specifically, the load reduction waveform determination unit 36 is configured to divide the time zone indicated by each divided time-series data Dv into a plurality of sections in the basic time zone corresponding to one cycle of the power supply current. When dividing, the basic waveform 95 specified by the specifying unit 23 is referred to. Further, the load reduction waveform determination unit 36 compares the maximum absolute value of the power supply current with the first reference value for each of the plurality of basic time zones. A power supply current whose maximum absolute value is smaller than the first reference value is determined to be a low current, and it is determined that the load reduction waveform 96 is included in the waveform 9.

[0042] The load reduction waveform determination unit 36 treats the divided time-series data Dv for which it is determined that the load reduction waveform 96 is included as the time-series data D1 of the non-target power supply current. The time-series data D1 is subjected to deletion processing by the data processing unit 29 (see FIG. 2).

[0043] According to the inventor's findings, the low current that can be measured in the operating state where the load instantaneously decreases during operation has a small absolute value compared to the normal power supply current measured while the load is present. Therefore, the power supply current including the low current can hinder accurate abnormality determination. In this regard, according to the above configuration, the time series data D1 of the non-target power supply current including the load decrease waveform 96 is treated as not being subject to abnormality determination. As a result, the abnormality determination unit 28 can accurately determine whether there is an abnormality in the motor 7.

[0044] Here, returning to FIG. 2, the time series data generation unit 15 may perform approximation processing on the measured time series data Dm generated by the A / D converter 3. The approximation processing is a process of setting to 0 the power supply current included in the dead zone R (see FIG. 11) where the absolute value is less than or equal to the third specified threshold value as the threshold value. The dead zone R is a region defined by two broken lines Su and Sd in FIG. 11 (the broken lines Su and Sd correspond to the third specified threshold value). The approximation processing is reflected in each of the plurality of divided time series data Dv.

[0045] The measured values of the power supply current that fluctuate near 0 are set to 0 in each of the divided time series data Dv. As a result, the load decrease waveform determination unit 36 can more accurately specify the current corresponding to one cycle (basic waveform 95) included in the power supply current indicated by the current time series data Da.

[0046] In order for the load decrease waveform determination unit 36 to accurately determine the presence or absence of the load decrease waveform 96, it is preferable that the specifying unit 23 accurately specifies the basic waveform 95. However, the specific basic waveform 94, which is only about half of the period of the power supply current, may be erroneously specified as the basic waveform 95. FIG. 11 shows two specific basic waveforms 94.

[0047] According to the inventor's findings, the period (dimension M1) of each specific basic waveform 94 is only about half of the period (dimension M0 in FIG. 10) of the normal basic waveform 95. The reason is as follows. The power supply current before the approximation process may fluctuate in the vicinity of the dead zone R. In this case, the power supply current may cross the boundaries (broken lines Su, Sd) defining the dead zone R multiple times in a short period. In particular, when the power supply is under inverter control, such data in the above state often occurs.

[0048] In the enlarged view of FIG. 11, the power supply current before the approximation process is indicated by a two-dot chain line N, and the power supply current after the approximation process is indicated by a solid line T. For example, when the power supply current (two-dot chain line N) before the approximation process crosses the broken line Sd multiple times in a short period, the power supply current (solid line T) after the approximation process takes a value of 0 multiple times in a short period. The specific part 23 identifies the falling point P1 or the rising point P2 each time the power supply current of the solid line T takes a value of 0. As a result, the specific basic waveform 94 is identified. Such incorrect point identification is likely to occur at the timing when the power supply current decreases from the maximum value to the dead zone R or increases from the minimum value to the dead zone R. Therefore, the period of the specific basic waveform 94 tends to be about 50% of the dimension M0.

[0049] Based on such findings, the load reduction waveform determination unit 36 in this example is configured to exclude the specific basic waveform 94 specified as the basic waveform 95 by the specific part 23 from the determination target. More specifically, when the plurality of basic waveforms 95 specified by the specific part 23 include those having a period less than 55% with respect to one period of the power supply current, the load reduction waveform determination unit 36 treats the basic waveform 95 as the specific basic waveform 94. Then, the load reduction waveform determination unit 36 excludes the divided time series data Dv including the specific basic waveform 94 from the determination target for whether low current is included.

[0050] According to the above configuration, a basic waveform 95 having a period of less than 55% with respect to one period of the power supply current is regarded as a specific basic waveform 94 and excluded from the determination target of whether low current is included. As a result, the load reduction waveform determination unit 36 can accurately divide the time zone indicated by the current time series data Da into a basic time zone corresponding to one period of the power supply current. Therefore, it is possible to accurately determine whether the load reduction waveform 96 is included in the waveform 9 of the power supply current.

[0051] In the enlarged view of FIG. 11, the solid line T and the two-dot chain line N outside the dead zone R actually coincide. In this figure, for the convenience of viewing the drawing, the two are shown separately.

[0052] <Details of the waveform determination unit 30 (third example)> FIG. 12 is a schematic diagram of a waveform determination unit 30C (30) according to the third example. In the third example, a load increase waveform 93 is included in the specific waveform 90 (see FIG. 13). The load increase waveform 93 indicates the operating state of the motor 7 in which the motor load has instantaneously increased during operation (which may be rated operation). In other words, the load increase waveform 93 can occur under limited conditions during the operation of the motor 7. When the motor load instantaneously increases during the operation of the motor 7, the torque required to drive the motor 7 instantaneously increases. In this case, the absolute value of the power supply current measured by the measuring device 6 instantaneously exceeds the second reference value as the threshold value. Hereinafter, such a power supply current may be referred to as "high current".

[0053] Here, the second reference value, which is the threshold value for detecting high current, is a value obtained by multiplying the peak value corresponding to 1.41 times the effective value of the power supply current by a second coefficient greater than 1. Although it is only an example, the second coefficient is a value of 1.1 or more and 1.3 or less. The peak value of the power supply current may differ among the plurality of divided time series data Dv. Therefore, the second reference value is specified in association with each of the plurality of divided time series data Dv. Although the second reference value is also set below the t-axis in FIG. 13, the illustration is omitted for the convenience of simplifying the drawing.

[0054] Referring to FIG. 12, a configuration for detecting the load increase waveform 93 will be described. The waveform determination unit 30C according to the third example includes a second acquisition unit 22 for acquiring a second reference value for each of a plurality of divided time series data Dv included in the current time series data Da. The second acquisition unit 22 acquires the second reference value by multiplying the peak value of the power supply current by a second coefficient for each divided time series data Dv. The second acquisition unit 22 also performs data processing for associating a plurality of second reference values with a plurality of divided time series data Dv respectively. The result of the data processing is output to a load increase waveform determination unit 35 (described later), which is a component of the waveform determination unit 30C.

[0055] The waveform determination unit 30C further includes a load increase waveform determination unit 35 for determining whether the load increase waveform 93 is included in the waveform 9 of the power supply current. The load increase waveform determination unit 35 is configured to determine that the load increase waveform 93 is included in the waveform 9 when a high current whose absolute value exceeds the second reference value is included in the power supply current indicated by each of the plurality of divided time series data Dv. The load increase waveform determination unit 35 treats the divided time series data Dv for which it is determined that the load increase waveform 93 is included as the time series data D1 of the non-target power supply current. The time series data D1 is subjected to deletion processing by the data processing unit 29 (see FIG. 2).

[0056] According to the inventor's findings, the high current that can be measured in the operating state where the load instantaneously increases during operation has a larger absolute value than the normal alternating current measured while the load is occurring. Therefore, the power supply current including the high current may hinder accurate abnormality determination. In this regard, according to the above configuration, the time series data D1 of the non-target power supply current including the load increase waveform 93 is treated as not being subject to abnormality determination. Thereby, the abnormality determination unit 28 can accurately determine whether there is an abnormality in the motor 7.

[0057] The waveform determination units 30A, 30B, and 30C described above may be arbitrarily combined. For example, the configuration for detecting the load decrease waveform 96 of the waveform determination unit 30B may be additionally applied to the waveform determination unit 30A, or the configuration for detecting the load increase waveform 93 of the waveform determination unit 30C may be additionally applied to the waveform determination unit 30A.

[0058] <History data generation unit 25> Returning to FIG. 2, the motor abnormality monitoring device 1 may further include a history data generation unit 25 configured to generate history data Dh. The history data Dh indicates the operating state of the motor 7 corresponding to the specific waveform 90 that has been excluded from the target of abnormality determination by the data processing unit 29. The history data generation unit 25 generates the history data Dh by referring to the specific waveform 90 detected by the waveform determination unit 30 and the time-series data D1 deleted by the data processing unit 29.

[0059] For example, when the waveform determination unit 30 has a function of detecting the load decrease waveform 96 and the load increase waveform 93, there are three types of time-series data D1 to be deleted by the data processing unit 29. First, the time-series data D1 including the load decrease waveform 96, second, the time-series data D1 including the load increase waveform 93, and third, the time-series data D1 including both the load decrease waveform 96 and the load increase waveform 93 are the targets of deletion by the data processing unit 29. The history data generation unit 25 identifies these three types of time-series data D1 deleted by the data processing unit 29 based on the determination result of the waveform determination unit 30. In this example, the history data Dh is generated by adding data indicating the identification result to each time-series data D1.

[0060] The history data Dh is referred to by the operator. Through the reference of the history data Dh, the operator can confirm the detection frequency of each of the load decrease waveform 96 and the load increase waveform 93. The operator may reset the first coefficient and the second coefficient based on the confirmation result in order to optimize the detection sensitivity of these specific waveforms 90. If the first coefficient increases, the detection sensitivity of the load decrease waveform 96 increases, and if the second coefficient increases, the detection sensitivity of the load increase waveform 93 decreases. Each reset command of the first coefficient and the second coefficient input by the operator is received by the motor abnormality monitoring device 1.

[0061] According to the above configuration, the operator can confirm whether the determination regarding the presence or absence of the specific waveform 90 in the waveform determination unit 30 is appropriate by checking the history data Dh generated by the history data generation unit 25. As a result, the operator can also change the set values (the first coefficient and the second coefficient) for detecting the specific waveform 90 set in the motor abnormality monitoring device 1.

[0062] <Abnormality monitoring process of motor 7> Referring to FIGS. 14 and 15, the abnormality monitoring process (abnormality monitoring method) of the motor 7 will be described. This process is executed by the processor of the motor abnormality monitoring device 1. Hereinafter, the abnormality monitoring process for detecting the load decrease waveform 96 (see FIG. 9) and the load increase waveform 93 (see FIG. 13) will be exemplified. Also, hereinafter, "step" may be abbreviated as "S".

[0063] As shown in FIG. 14, first, the processor executes a current time-series data generation step of generating the current time-series data Da (S11). The processor that executes S11 is an example of the time-series data generation unit 15.

[0064] Next, the processor executes a waveform determination step of determining whether the waveform 9 of the power supply current indicated by the current time-series data Da includes the specific waveform 90 that is not the object of abnormality monitoring (S13). The processor that executes S13 is an example of the waveform determination unit 30.

[0065] Referring to FIG. 15, the details of the waveform determination step are illustrated. The processor executes a first acquisition step of acquiring a first reference value for a plurality of divided time series data Dv included in the current time series data Da (S31). The processor that executes S31 is an example of the first acquisition unit 21. Next, the processor executes a specification step for specifying a plurality of basic waveforms 95 each regarded as one cycle of the power supply current for the plurality of divided time series data Dv included in the current time series data Da (S33). The processor that executes S33 is an example of the specification unit 23.

[0066] Next, the processor determines whether the load reduction waveform 96 is included in the waveform 9 for each of the plurality of divided time series data Dv (S35). The processor that executes S35 is an example of the load reduction waveform determination unit 36. If it is determined that the load reduction waveform determination unit 36 is not included (S35: NO), the processor transfers the process to S39. If it is determined that the load reduction waveform determination unit 36 is included (S35: YES), the processor regards the divided time series data Dv for which it is determined that the load reduction waveform determination unit 36 is included as the time series data D1 (S37). That is, the time series data D1 is generated.

[0067] Next, the processor executes a second acquisition step of acquiring a second reference value for the plurality of divided time series data Dv included in the current time series data Da (S39). At this time, the second reference value is also acquired for the divided time series data Dv regarded as the time series data D1 in S37. The processor that executes S39 is an example of the second acquisition unit 22.

[0068] Next, the processor determines, for each of the plurality of divided time-series data Dv, whether the load increase waveform 93 is included in the waveform 9 (S41). The time-series data D1 generated in S37 is also included in the determination target of S41. The processor that executes S41 is an example of the load increase waveform determination unit 35. If it is determined that the load increase waveform 93 is not included (S41: NO), the processor ends the specific waveform determination process and shifts the process to S15 in FIG. 14. If it is determined that the load increase waveform 93 is included (S41: YES), the processor regards the divided time-series data Dv in which the load increase waveform 93 is included as the time-series data D1 (S43). That is, the time-series data D1 is generated. Here, if the load increase waveform 93 is included in the time-series data D1 generated in S37, data processing indicating that two specific waveforms 90 are included is performed on the time-series data D1. Thereafter, the processor ends the specific waveform determination process.

[0069] Returning to FIG. 14, the processor executes a data processing step (S15). In S15, when it is determined that at least one of the load decrease waveform 96 or the load increase waveform 93 is included in the waveform 9 of the power supply current in S13, the processor executes data processing that excludes the time-series data D1 generated in S37 and S43 from the abnormality determination target. More specifically, the processor executes a process of deleting the time-series data D1. Note that if neither the load decrease waveform 96 nor the load increase waveform 93 is detected in S13, S15 is skipped. The processor that executes S15 is an example of the data processing unit 29.

[0070] Next, the processor executes an abnormality determination step of determining whether there is an abnormality in the motor 7 based on the plurality of divided time-series data Dv (S17). If it is determined that there is an abnormality, the processor may execute a display control process of causing a monitor provided in the motor abnormality monitoring device 1 to display an error message. The processor that executes S17 is an example of the abnormality determination unit 28.

[0071] Next, the processor executes a history data generation step (S19) for generating history data Dh. The history data Dh in this example indicates the operating state of the motor 7 corresponding to the load decrease waveform 96 or the load increase waveform 93 that is excluded from the abnormality determination by S19. The generated history data Dh may be displayed on the monitor of the motor abnormality monitoring device 1. Note that in S13, if neither the load decrease waveform 96 nor the load increase waveform 93 is detected, S19 is skipped. The processor that executes S19 is an example of the history data generation unit 25.

[0072] Next, the processor executes a first reception step (S21) for receiving an adjustment command for the first coefficient executed based on the generated history data Dh, and a second reception step (S23) for receiving an adjustment command for the second coefficient executed based on the generated history data Dh. In S21 and S23, an operator who has confirmed the history data Dh inputs an adjustment command to the motor abnormality monitoring device 1, and the adjustment command is received by the processor. The processor executes control to change the first coefficient and the second coefficient based on the received adjustment command. Then, this control process ends.

[0073] According to the above configuration, it becomes possible to adjust the first coefficient and the second coefficient as set values based on the generated history data Dh. Therefore, the operator can change the first coefficient and the second coefficient so that the load decrease waveform 96 and the load increase waveform 93 are detected more appropriately.

[0074] <Modification Example> The time-series data generation unit 15 does not necessarily generate a plurality of divided time-series data Dv from the current time-series data Da. In this case, based on the current time-series data Da, the waveform determination unit 30 may determine whether the specific waveform 90 is included in the waveform 9. Also, the data processing excluded from the target of the abnormality determination executed by the data processing unit 29 is not limited to the deletion processing. For example, a data table indicating the time zone in which the specific waveform 90 occurs may be separately generated from among the time zones indicated by the current time-series data Da. The abnormality determination unit 28 specifies the normal time zone of the current time-series data Da excluding the time zone indicated by the data table. The abnormality determination unit 28 may determine whether there is an abnormality in the motor 7 based on the spectrum data in the normal time zone.

[0075] Also, in the above-described embodiment, an example in which the object of the abnormality monitoring is the motor 7 has been described, but a generator may be the object of the monitoring instead of the motor 7. When the specific waveform 90 is included in the waveform 9 depicted by the alternating current of the generator (that is, the alternating current generated by power generation), the specific waveform 90 is treated as being excluded from the target of the abnormality determination. Thereby, it becomes possible to accurately determine whether there is an abnormality in the generator. The motor 7 and the generator are examples of the "rotating electrical machine" of the present invention.

[0076] <Summary> The content described in several of the above-described embodiments is grasped as follows, for example.

[0077] 1) An abnormality monitoring device for a rotating electrical machine (motor abnormality monitoring device 1) according to an embodiment of the present disclosure is An abnormality monitoring device for a rotating electrical machine, comprising an abnormality determination unit (28) for determining whether there is an abnormality in the rotating electrical machine based on current time-series data (Da) indicating a temporal change in alternating current (power supply current) in the rotating electrical machine (motor 7 or generator), A waveform determination unit (30) for determining whether a specific waveform (90) indicating an operating state of the rotating electrical machine, which is excluded from the target of the abnormality monitoring, is included in the waveform (9) of the alternating current indicated by the current time-series data, When it is determined that the specific waveform is included, a data processing unit (29) for executing data processing for excluding time-series data (D1) of non-target alternating current (non-target power supply current) including the alternating current corresponding to the specific waveform from being a target of abnormality determination by the abnormality determination unit is provided. is provided.

[0078] According to the configuration of 1) above, when a specific waveform indicating an operating state that is not a target of abnormality monitoring is included in the waveform of the alternating current indicated by the current time-series data during the operating state of the rotating electrical machine, the time-series data of the non-target alternating current including the alternating current corresponding to the specific waveform is treated as not being a target of abnormality determination by the abnormality determination unit. As a result, the abnormality determination unit can determine the presence or absence of an abnormality based on the current time-series data suitable for abnormality determination. Therefore, an abnormality monitoring device for a rotating electrical machine that can accurately determine whether there is an abnormality in the rotating electrical machine is realized.

[0079] 2) In some embodiments, there is provided an abnormality monitoring device for a rotating electrical machine according to 1) above, wherein the specific waveform has a sudden waveform (98) indicating a starting operation state of a motor (7) as the rotating electrical machine or an operation state in which a load increase temporarily occurs in the motor, and the waveform determination unit includes a sudden waveform determination unit (30) configured to determine that the sudden waveform is included in the waveform of the alternating current when the alternating current indicated by the current time-series data includes a sudden current whose absolute value exceeds a first specified threshold value.

[0080] According to the inventor's findings, the inrush current that can be measured while the motor is in the starting operation state or the current that changes rapidly due to an increase in the load (load fluctuation) of the motor has a very high absolute value. Therefore, the alternating current including the sudden current can interfere with accurate abnormality determination. In this regard, according to the configuration of 2) above, the time-series data of the non-target alternating current corresponding to the sudden waveform is treated as not being a target of abnormality determination. As a result, the abnormality determination unit can accurately determine whether there is an abnormality in the motor.

[0081] 3) In some embodiments, there is provided an abnormal monitoring device for a rotating electrical machine according to 1) or 2) above, wherein the specific waveform has a start / stop waveform (97) indicating either a start-up operation state or a stop operation state of a motor (7) as the rotating electrical machine, and the waveform determination unit includes a start / stop waveform determination unit (30) for determining that the start / stop waveform is included in the waveform of the alternating current when the alternating current indicated by the current time series data includes a weak current having an absolute value less than or equal to a second specified threshold value for a predetermined time or more.

[0082] According to the inventors' findings, since the weak current that can be measured while the motor is in the start-up operation state or the stop operation state has a very small value for a predetermined time or more, the alternating current including the weak current may interfere with accurate abnormality determination. In this regard, according to the configuration of 3) above, the time series data of the non-target alternating current including the start / stop waveform is treated as being outside the scope of abnormality determination. Thereby, the abnormality determination unit can accurately determine whether there is an abnormality in the motor.

[0083] 4) In some embodiments, there is provided an abnormal monitoring device for a rotating electrical machine according to 1) or 3) above, wherein the specific waveform has a load reduction waveform (96) indicating the operation state of a motor (7) as the rotating electrical machine in which the load temporarily decreases during operation (which may be but is not limited to rated operation), and the waveform determination unit includes a first acquisition unit (21) for acquiring a first reference value obtained by multiplying the peak value of the alternating current indicated by the current time series data by a first coefficient smaller than 1, and is configured to divide the time zone indicated by the current time series data into a plurality of basic time zones corresponding to one cycle of the alternating current, and when the alternating current in any of the plurality of basic time zones includes a low current having a maximum absolute value smaller than the first reference value, determine that the load reduction waveform is included in the waveform of the alternating current.

[0084] According to the inventor's findings, the low current that can be measured in an operating state where the load instantaneously decreases during operation has a smaller absolute value compared to the normal alternating current measured while the load (which may be the rated load but is not limited thereto) is generated. Therefore, the alternating current including the low current can interfere with accurate abnormality determination. In this regard, according to the configuration of 4) above, the time-series data of the non-target alternating current including the load decrease waveform is treated as not being subject to abnormality determination. Thereby, the abnormality determination unit can accurately determine whether there is an abnormality in the motor.

[0085] 5) In some embodiments, there is provided an abnormality monitoring device for a rotating electrical machine as described in 4) above, a time-series data generation unit (15) for generating the current time-series data is further provided by performing an approximation process of setting to 0 the alternating current included in a dead zone (R) where the absolute value is equal to or less than a third specified threshold value with respect to the measurement time-series data indicating the change over time of the measured value of the alternating current.

[0086] According to the configuration of 5) above, the measured value of the alternating current that fluctuates in the vicinity of 0 is set to 0 in the current time-series data. Thereby, the load decrease waveform determination unit can accurately identify the current corresponding to one cycle included in the alternating current indicated by the current time-series data.

[0087] 6) In some embodiments, there is provided an abnormality monitoring device for a rotating electrical machine as described in 5) above, a specifying unit (23) for specifying a plurality of basic waveforms (95) each regarded as one cycle of the alternating current is further provided in the waveform of the alternating current indicated by the current time-series data, when the specified plurality of basic waveforms include a specific basic waveform (94) having a period less than 55% with respect to one cycle of the alternating current, the load decrease waveform determination unit is configured to exclude the specific basic waveform from the determination target of whether the low current is included.

[0088] According to the inventor's findings, even if a specific part identifies a plurality of basic waveforms based on the current time-series data subjected to approximation processing, the basic waveforms may include specific current waveforms that are inappropriate as objects for determining whether low current is included. This is because the measured value of the alternating current included in the measured time-series data fluctuates in the vicinity of the dead zone. Therefore, the period of the specific current waveform is about 50% of one period of the alternating current. In this regard, according to the configuration of 6) above, a basic waveform having a period of less than 55% of one period of the alternating current is regarded as a specific basic waveform and is excluded from the objects for determining whether low current is included. As a result, the load reduction determination unit can accurately divide the time zone indicated by the current time-series data into basic time zones corresponding to one period of the alternating current. Thereby, it is possible to accurately determine whether the load reduction waveform is included in the waveform of the alternating current. In particular, in an inverter power supply, this measure is very effective.

[0089] 7) In some embodiments, there is provided an abnormal monitoring device for a rotating electrical machine according to any one of 1) to 6) above, wherein the specific waveform has a load increase waveform (93) indicating the operating state of the motor (7) as the rotating electrical machine in which the load temporarily increases during operation (which may be but is not limited to rated operation), and the waveform determination unit includes a second acquisition unit (22) for acquiring a second reference value obtained by multiplying the peak value of the alternating current indicated by the current time-series data by a second coefficient greater than 1, and a load increase waveform determination unit (30) for determining that the load increase waveform is included in the waveform of the alternating current when the alternating current indicated by the current time-series data includes a high current whose absolute value exceeds the second reference value.

[0090] According to the inventor's findings, the high current that can be measured in the operating state where the load instantaneously increases during operation has a larger absolute value compared to the normal alternating current measured while the load (which may or may not be the rated load, but is not limited thereto) is occurring. Therefore, the alternating current including the high current can hinder accurate abnormality determination. In this regard, according to the configuration of 7) above, the time-series data of the non-target alternating current including the load increase waveform is treated as being outside the scope of abnormality determination. Thereby, the abnormality determination unit can accurately determine whether there is an abnormality in the motor.

[0091] 8) In some embodiments, there is an abnormality monitoring device for a rotating electrical machine according to any one of 1) to 7) above, further comprising a history data generation unit (25) for generating history data (Dh) indicating the operating state of the rotating electrical machine corresponding to the specific waveform that has been made outside the scope of the abnormality determination by the data processing unit.

[0092] According to the configuration of 8) above, the operator of the rotating electrical machine can confirm whether the determination regarding the presence or absence of the specific waveform in the waveform determination unit was appropriate by checking the history data generated by the history data generation unit. Thereby, the operator can also change the set value for detecting the specific waveform set in the abnormality monitoring device for the rotating electrical machine.

[0093] 9) An abnormality monitoring method for a rotating electrical machine according to at least one embodiment of the present disclosure is an abnormality monitoring method for a rotating electrical machine comprising an abnormality determination step (S17) for determining whether there is an abnormality in the rotating electrical machine based on current time-series data (Da) indicating the change over time of the alternating current (supply current) in the rotating electrical machine (for example, motor 7), a waveform determination step (S13) for determining whether a specific waveform (90) indicating the operating state of the rotating electrical machine that is outside the scope of abnormality monitoring is included in the waveform (9) of the alternating current indicated by the current time-series data, When it is determined that the specific waveform is included, a data processing step (S15) is executed to perform data processing that excludes the time-series data (D1) of the non-target alternating current, which is the alternating current including the specific waveform, from the target of the abnormality determination in the abnormality determination step. is provided.

[0094] According to the configuration of 9) above, the same technical advantages as those of 1) above can be obtained.

[0095] 10) In some embodiments, there is provided an abnormality monitoring method for a rotating electrical machine according to 9) above, wherein the specific waveform has a load reduction waveform indicating the operating state of the motor (7) as the rotating electrical machine in which the load temporarily decreases during rated operation, the waveform determination step includes a first acquisition step (S31) of acquiring a first reference value obtained by multiplying the peak value of the alternating current indicated by the current time-series data by a first coefficient smaller than 1, a load reduction waveform determination step (S35) of dividing the time zone indicated by the current time-series data into a plurality of basic time zones corresponding to one cycle of the alternating current, and determining that the load reduction waveform is included in the waveform of the alternating current when a low current having a maximum absolute value smaller than the first reference value is included in any of the plurality of basic time zones of the alternating current.

[0096] According to the configuration of 10) above, the same technical advantages as those of 4) above can be obtained.

[0097] 11) In some embodiments, there is provided an abnormality monitoring method for a rotating electrical machine according to 10) above, a history data generation step (S19) for generating history data indicating the operating state of the motor (7) corresponding to the specific waveform excluded from the target of the abnormality determination by the data processing step, a first reception step (S21) of receiving an adjustment command for the first coefficient executed based on the generated history data and further includes.

[0098] According to the configuration of 11) above, it becomes possible to adjust the first coefficient as a set value based on the generated history data. Therefore, the operator of the motor can change the first coefficient so that the load reduction waveform is detected more appropriately.

[0099] 12) In some embodiments, there is an abnormal monitoring method for a rotating electrical machine according to any one of 9) to 11) above, The specific waveform has a load increase waveform indicating the operating state of the motor (7) as the rotating electrical machine in which the load temporarily increases during rated operation, The waveform determination step includes: a second acquisition step (S23) of acquiring a second reference value obtained by multiplying the peak value of the alternating current indicated by the current time series data by a second coefficient greater than 1; a load increase waveform determination step (S41) of determining that the load increase waveform is included in the waveform of the alternating current when the alternating current indicated by the current time series data includes a high current whose absolute value exceeds the second reference value.

[0100] According to the configuration of 12) above, the same technical advantages as those of 7) above can be obtained.

[0101] 13) In some embodiments, there is an abnormal monitoring method for a rotating electrical machine according to 12) above, a history data generation step (S19) for generating history data (Dh) indicating the operating state of the motor (7) corresponding to the specific waveform excluded from the target of the abnormality determination by the data processing step; a second reception step (S23) of receiving an adjustment command for the second coefficient executed based on the generated history data and further includes.

[0102] According to the configuration of 13) above, it becomes possible to adjust the second coefficient as a set value based on the generated history data. Therefore, the operator of the motor can change the second coefficient so that the load increase waveform is detected more appropriately.

Explanation of Signs

[0103] 1: Motor Abnormality Monitoring Device 2: Power Supply Equipment 3: A / D Converter 4: Electric Wire 6: Measuring Device 7: Motor 8: Mechanical Device 9: Waveform 10: Abnormality Monitoring System 15: Time-Series Data Generation Unit 21: First Acquisition Unit 22: Second Acquisition Unit 23: Specifying Unit 25: History Data Generation Unit 28: Abnormality Judgment Unit 29: Data Processing Unit 30: Waveform Judgment Unit 35: Load Increase Waveform Judgment Unit 36: Load Decrease Waveform Judgment Unit 37: Start / Stop Waveform Judgment Unit 38: Sudden Waveform Judgment Unit 90: Specific Waveform 93: Load Increase Waveform 94: Specific Basic Waveform 95: Basic Waveform 96: Load Decrease Waveform 97: Start / Stop Waveform 98: Sudden Waveform D1: Time-Series Data Da: Current Time-Series Data Dh: History Data Dm: Measurement Time-Series Data Dv: Divided Time-Series Data L, M0, M1: Dimensions N: Two-Dot Chain Line P1: Rising Point P2: Falling Point R: Dead Band Region Sd, Su: Dashed Line T: Solid Line

Claims

1. An abnormal monitoring device for a rotating electrical machine, comprising an abnormality determination unit for determining whether there is an abnormality in the rotating electrical machine based on current time-series data indicating a change over time of an alternating current in the rotating electrical machine, a waveform determination unit for determining whether a specific waveform indicating an operating state of the rotating electrical machine, which is not an object of abnormal monitoring, is included in the waveform of the alternating current indicated by the current time-series data, and a data processing unit for executing data processing to exclude time-series data of an off-target alternating current including the alternating current corresponding to the specific waveform from being an object of abnormality determination by the abnormality determination unit when it is determined that the specific waveform is included. An abnormal monitoring device for a rotating electrical machine, comprising the above components.

2. The specific waveform has a burst waveform indicating a starting operation state of a motor as the rotating electrical machine or an operating state in which a load temporarily increases on the motor, and the waveform determination unit includes a burst waveform determination unit configured to determine that the burst waveform is included in the waveform of the alternating current when the alternating current indicated by the current time-series data includes a burst current whose absolute value exceeds a first specified threshold value. The abnormal monitoring device for a rotating electrical machine according to Claim 1.

3. The specific waveform has a start / stop waveform indicating either a starting operation state or a stopping operation state of a motor as the rotating electrical machine, and the waveform determination unit includes a start / stop waveform determination unit for determining that the start / stop waveform is included in the waveform of the alternating current when the alternating current indicated by the current time-series data includes a weak current whose absolute value is equal to or less than a second specified threshold value for a predetermined time or more. The abnormal monitoring device for a rotating electrical machine according to Claim 1 or 2.

4. The specific waveform has a load reduction waveform indicating the operating state of a motor as the rotating electrical machine in which the load has temporarily decreased during operation, and the waveform determination unit includes a first acquisition unit for acquiring a first reference value obtained by multiplying the peak value of the alternating current indicated by the current time-series data by a first coefficient smaller than 1, and a load reduction waveform determination unit configured to divide the time zone indicated by the current time-series data into a plurality of basic time zones corresponding to one cycle of the alternating current, and to determine that the load reduction waveform is included in the waveform of the alternating current when a low current having a maximum absolute value smaller than the first reference value is included in the alternating current in any of the plurality of basic time zones. The abnormal monitoring device for a rotating electrical machine according to Claim 1 or 2.

5. For the measurement time-series data indicating the change over time of the measured value of the alternating current, an approximation process is performed to set the alternating current included in the dead zone where the absolute value is equal to or less than the third specified threshold value to zero, thereby further comprising a time-series data generation unit for generating the current time-series data The abnormal monitoring device for a rotating electrical machine according to claim 4

6. further comprising a specifying unit for specifying a plurality of basic waveforms each regarded as one cycle of the alternating current in the waveform of the alternating current indicated by the current time-series data When the specified plurality of basic waveforms include a specific basic waveform having a period of less than 55% with respect to one cycle of the alternating current, the load decrease waveform determination unit is configured to exclude the specific basic waveform from the determination target of whether the low current is included The abnormal monitoring device for a rotating electrical machine according to claim 5

7. The specific waveform has a load increase waveform indicating the operating state of the motor as the rotating electrical machine in which the load temporarily increases during rated operation The waveform determination unit a second acquisition unit for acquiring a second reference value obtained by multiplying the peak value of the alternating current indicated by the current time-series data by a second coefficient greater than 1; a load increase waveform determination unit for determining that the load increase waveform is included in the waveform of the alternating current when the alternating current indicated by the current time-series data includes a high current whose absolute value exceeds the second reference value The abnormal monitoring device for a rotating electrical machine according to claim 1 or 2

8. further comprising a history data generation unit for generating history data indicating the operating state of the rotating electrical machine corresponding to the specific waveform excluded from the target of the abnormality determination by the data processing unit The abnormal monitoring device for a rotating electrical machine according to claim 1 or 2

9. An abnormal monitoring method for a rotating electrical machine, comprising an abnormality determination step for determining whether there is an abnormality in the rotating electrical machine based on current time-series data indicating the change over time of the alternating current in the rotating electrical machine, a waveform determination step of determining whether the waveform of the alternating current indicated by the current time-series data includes a specific waveform indicating the operating state of the rotating electrical machine excluded from the target of abnormal monitoring; a data processing step of executing data processing for excluding the time-series data of the non-target alternating current, which is the alternating current including the specific waveform, from the target of the abnormality determination in the abnormality determination step when it is determined that the specific waveform is included An abnormal monitoring method for a rotating electrical machine comprising the above steps

10. The specific waveform has a load decrease waveform indicating the operating state of the motor as the rotating electrical machine in which the load temporarily decreases during operation, The waveform determination step includes: a first acquisition step of acquiring a first reference value obtained by multiplying the peak value of the alternating current indicated by the current time series data by a first coefficient smaller than 1; a load decrease waveform determination step of dividing the time zone indicated by the current time series data into a plurality of basic time zones corresponding to one cycle of the alternating current, and determining that the load decrease waveform is included in the waveform of the alternating current when a low current having an absolute value smaller than the first reference value is included in the alternating current in any of the plurality of basic time zones. The abnormal monitoring method for a rotating electrical machine according to claim 9.

11. a history data generation step for generating history data indicating the operating state of the motor corresponding to the specific waveform excluded from the target of the abnormality determination by the data processing step; a first reception step of receiving an adjustment command for the first coefficient executed based on the generated history data and further includes The abnormal monitoring method for a rotating electrical machine according to claim 10.

12. The specific waveform has a load increase waveform indicating the operating state of the motor as the rotating electrical machine in which the load temporarily increases during operation, The waveform determination step includes: a second acquisition step of acquiring a second reference value obtained by multiplying the peak value of the alternating current indicated by the current time series data by a second coefficient larger than 1; a load increase waveform determination step of determining that the load increase waveform is included in the waveform of the alternating current when a high current having an absolute value exceeding the second reference value is included in the alternating current indicated by the current time series data. The abnormal monitoring method for a rotating electrical machine according to any one of claims 9 to 11.

13. a history data generation step for generating history data indicating the operating state of the motor corresponding to the specific waveform excluded from the target of the abnormality determination by the data processing step; a second reception step of receiving an adjustment command for the second coefficient executed based on the generated history data and further includes The abnormal monitoring method for a rotating electrical machine according to claim 12.

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