Abnormality diagnosis device, abnormality diagnosis system, abnormality diagnosis method and program

Abstract

An abnormality diagnosis device (101) according to the present disclosure comprises: an electric current signal storage unit (21A) that stores an electric current signal of an electric motor (5); a frequency analysis unit (22A) that performs a frequency analysis of a waveform of the electric current signal; a characteristic frequency band extraction unit (22B) for extracting data belonging to a characteristic frequency band so that the frequency analysis result includes a plurality of spectrum peaks in a preset frequency range; a characteristic amount calculation unit (221C) that detects the plurality of spectrum peaks from the data belonging to the characteristic frequency band, excludes, from the data belonging to the characteristic frequency band, data detected as the plurality of spectrum peaks, and calculates the total sum of signal intensities included in the detected data belonging to the characteristic frequency band having undergone data exclusion; and an abnormality diagnosis unit (22D) that, when the total sum is not less than a first threshold value, diagnoses an abnormality of a rotary machine facility. Accordingly, it is possible to diagnose, with high accuracy, an abnormality that is caused by a failure mode in conjunction with minute torque fluctuation. Further, the diagnosis result may be transmitted to a plurality of the rotary machine facilities connected over a network.

Description

[Technical Field]

[0001] The present disclosure relates to an abnormality diagnosis device, an abnormality diagnosis system, an abnormality diagnosis method, and a program. [Background technology]

[0002] Conventionally, there are many rotating machinery facilities that are composed of electric motors and load facilities that use the electric motors as their power source, such as pumps, fans, blowers, etc. For example, abnormality diagnosis of electric motors and load facilities is performed by detecting signal strength fluctuations due to abnormalities from the results of frequency analysis of the drive current of the electric motor.

[0003] However, minute torque fluctuations in electric motors that occur in conjunction with failure modes such as pump cavitation, foreign matter or air entrapment, and other conditions are unlikely to have periodicity and therefore do not easily appear as specific spectral peaks in the results of frequency analysis of the drive current waveform, making it difficult to diagnose abnormalities due to failure modes that involve minute torque fluctuations.

[0004] Therefore, methods for diagnosing abnormalities due to failure modes accompanied by minute torque fluctuations have been studied.For example, in the abnormality diagnosis device disclosed in Patent Document 1, the drive current of the electric motor is frequency analyzed, the fundamental wave and harmonics are excluded from a preset frequency range, and then a preset number of intensity values ​​from the highest in the frequency range are summed to calculate the degree of deterioration, thereby diagnosing an abnormality in the electric motor when an abnormality caused by cavitation occurs. [Prior art documents] [Patent documents]

[0005] Patent Publication No. 2020-153965 Summary of the Invention [Problem to be solved by the invention]

[0006] However, with the method of Patent Document 1, if a failure mode other than cavitation or noise caused by inverter drive occurs simultaneously with cavitation, spectral peaks other than the fundamental wave and harmonics will occur within a preset frequency range, and these spectral peaks will be included in the sum of intensity values. This reduces the accuracy of detecting minute torque fluctuations, making it difficult to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.

[0007] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an abnormality diagnosis device and the like that can accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations. [Means for solving the problem]

[0008] An abnormality diagnosis device according to the present disclosure is an abnormality diagnosis device for diagnosing an abnormality in rotating machinery equipment, and includes: a current signal storage unit that stores a current signal of an electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include multiple spectral peaks within a predetermined frequency range from a frequency analysis result obtained by the frequency analysis unit frequency analyzing the waveform of the current signal; a feature quantity calculation unit that detects the multiple spectral peaks from the data belonging to the characteristic frequency band, excludes data detected as the multiple spectral peaks from the data belonging to the characteristic frequency band, and calculates a sum of signal strengths included in the data belonging to the characteristic frequency band excluding the data detected as the multiple spectral peaks; and an abnormality diagnosis unit that diagnoses an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold. The first threshold value is a value obtained by performing statistical processing on the sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and when the number of data in the sum is different from the number of data in the first threshold value, the feature calculation unit removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by:

[0009] An abnormality diagnosis device according to the present disclosure is an abnormality diagnosis device that diagnoses abnormalities in rotating machinery equipment, and includes: a current signal storage unit that stores current signals of an electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a feature frequency band extraction unit that extracts data belonging to a feature frequency band so as to include multiple spectrum peaks within a predetermined frequency range from a frequency analysis result obtained by the frequency analysis unit frequency analyzing the waveform of the current signal; a feature quantity calculation unit that sorts the data belonging to the feature frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band, and calculates a sum of signal strengths included in the data belonging to the feature frequency band from which the data whose signal strength is equal to or greater than the second threshold has been excluded; and an abnormality diagnosis unit that diagnoses an abnormality in the rotating machinery equipment if the sum is equal to or greater than a first threshold. The first threshold value is a value obtained by performing statistical processing on the sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and when the number of data in the sum is different from the number of data in the first threshold value, the feature calculation unit removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0010] An abnormality diagnosis system according to the present disclosure is an abnormality diagnosis system for diagnosing an abnormality in rotating machinery equipment, and includes: a current signal storage unit that stores a current signal of an electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include multiple spectral peaks within a predetermined frequency range from a frequency analysis result obtained by the frequency analysis unit frequency analyzing the waveform of the current signal; a feature quantity calculation unit that detects the multiple spectral peaks from the data belonging to the characteristic frequency band, excludes data detected as the multiple spectral peaks from the data belonging to the characteristic frequency band, and calculates a sum of signal intensities included in the data belonging to the characteristic frequency band excluding the data detected as the multiple spectral peaks; and an abnormality diagnosis unit that diagnoses an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold. The first threshold value is a value obtained by performing statistical processing on the sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and when the number of data in the sum is different from the number of data in the first threshold value, the feature calculation unit removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0011] An abnormality diagnosis system according to the present disclosure is an abnormality diagnosis system for diagnosing an abnormality in rotating machinery equipment, and includes: a current signal storage unit that stores a current signal of an electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a feature frequency band extraction unit that extracts data belonging to a feature frequency band so as to include multiple spectrum peaks within a predetermined frequency range from a frequency analysis result obtained by the frequency analysis unit frequency analyzing the waveform of the current signal; a feature quantity calculation unit that sorts the data belonging to the feature frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band, and calculates a sum of signal strengths included in the data belonging to the feature frequency band from which the data whose signal strength is equal to or greater than the second threshold has been excluded; and an abnormality diagnosis unit that diagnoses an abnormality in the rotating machinery equipment if the sum is equal to or greater than a first threshold. The first threshold value is a value obtained by performing statistical processing on the sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and when the number of data in the sum is different from the number of data in the first threshold value, the feature calculation unit removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0012] The abnormality diagnosis method according to the present disclosure includes a current signal detection step of detecting a current signal flowing through an electric motor, a frequency analysis step of frequency-analyzing a waveform of the current signal detected in the current signal detection step, a characteristic frequency band extraction step of extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency-analyzing the waveform of the current signal in the frequency analysis step, a data excluding step of detecting the plurality of spectral peaks from the data belonging to the characteristic frequency band and excluding data detected as the plurality of spectral peaks from the data belonging to the characteristic frequency band, a feature amount calculation step of calculating a sum of signal strengths included in the data belonging to the characteristic frequency band from which the data detected as the plurality of spectral peaks has been excluded, a determination step of determining whether the sum is equal to or greater than a first threshold, and an abnormality diagnosis step of diagnosing an abnormality in the rotating machine equipment if the sum is equal to or greater than the first threshold. The first threshold value is a value obtained by performing statistical processing on a sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and the feature calculation step, when the number of data in the sum is different from the number of data in the first threshold value, removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0013] An abnormality diagnosis method according to the present disclosure is an abnormality diagnosis method for diagnosing an abnormality in rotating machinery equipment, the method including: a current signal detection step of detecting a current signal flowing in an electric motor; a frequency analysis step of frequency-analyzing a waveform of the current signal detected in the current signal detection step; a feature frequency band extraction step of extracting data belonging to a feature frequency band so as to include a plurality of spectrum peaks within a predetermined frequency range from a frequency analysis result obtained by frequency-analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of sorting the data belonging to the feature frequency band in order of signal strength and excluding data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band; a feature amount calculation step of calculating a sum of signal strengths included in the data belonging to the feature frequency band from which the data whose signal strength is equal to or greater than the second threshold has been excluded; a determination step of determining whether the sum is equal to or greater than a first threshold; and an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment if the sum is equal to or greater than the first threshold. The first threshold value is a value obtained by performing statistical processing on a sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and the feature calculation step, when the number of data in the sum is different from the number of data in the first threshold value, removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0014] A program according to the present disclosure is a program for diagnosing an abnormality in rotating machinery equipment, and causes a computer to execute the following: a current signal detection step of detecting a current signal flowing in an electric motor; a frequency analysis step of frequency-analyzing the waveform of the current signal detected in the current signal detection step; a feature frequency band extraction step of extracting data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency-analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of detecting multiple spectral peaks from the data belonging to the feature frequency band and excluding data detected as the multiple spectral peaks from the data belonging to the feature frequency band; a feature amount calculation step of calculating a sum of signal strengths included in the data belonging to the feature frequency band from which the data detected as the multiple spectral peaks have been excluded; a determination step of determining whether the sum is equal to or greater than a first threshold; and an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment if the sum is equal to or greater than the first threshold. The first threshold value is a value obtained by performing statistical processing on a sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and the feature calculation step, when the number of data in the sum is different from the number of data in the first threshold value, removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following.

[0015] A program according to the present disclosure is a program for diagnosing abnormalities in rotating machinery equipment, and includes the following steps in a computer: a current signal detection step for detecting a current signal flowing in an electric motor; a frequency analysis step for performing frequency analysis on the waveform of the current signal detected in the current signal detection step; and a characteristic frequency band extraction step for extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from the frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step. a data excluding step of sorting data belonging to a characteristic frequency band in order of signal strength and excluding data having a signal strength equal to or greater than a second threshold from the data belonging to the characteristic frequency band; and a feature calculation step of calculating a sum of signal strengths included in the data belonging to the characteristic frequency band, from which data having a signal strength equal to or greater than the second threshold have been excluded. a determination step of determining whether the sum is equal to or greater than a first threshold value, and an abnormality diagnosis step of diagnosing that the rotating machinery equipment is abnormal if the sum is equal to or greater than the first threshold value. The first threshold value is a value obtained by performing statistical processing on a sum accumulated during a predetermined period from the start of operation of the rotating machinery equipment, and the feature calculation step, when the number of data in the sum is different from the number of data in the first threshold value, removes data with higher signal strength from the data in the first threshold value in order, or removes data with higher signal strength from the data in the sum, in order, to match the number of data in the sum with the number of data in the first threshold value. It is characterized by the following. [Effects of the Invention]

[0016] According to the present disclosure, an abnormality due to a failure mode accompanied by minute torque fluctuations can be diagnosed with high accuracy. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram showing a schematic configuration of an abnormality diagnosis device according to a first embodiment. [Figure 2] 1 is a diagram showing a schematic configuration of an abnormality diagnosis device according to a first embodiment. [Figure 3] 3 is a diagram showing a schematic configuration of a diagnosis result output unit according to the first embodiment. FIG. [Figure 4] 2 is a diagram illustrating a hardware configuration of a monitoring and diagnosing unit according to the first embodiment. FIG. [Figure 5] 4 is a flowchart showing a processing flow of the abnormality check device according to the first embodiment. [Figure 6] 4 is a diagram showing an example of a waveform of a current signal measured by a current detection unit according to the first embodiment. FIG. [Figure 7] 5 is a diagram showing an example of a frequency analysis result of a current signal measured by a current detection unit according to the first embodiment. FIG. [Figure 8] 5 is a diagram showing an example of a frequency analysis result of a current signal measured by a current detection unit according to the first embodiment. FIG. [Figure 9] 5 is a diagram showing an example of a frequency analysis result of a current signal measured by a current detection unit according to the first embodiment. FIG. [Figure 10] 4 is a diagram showing another schematic configuration of the abnormality diagnostic device according to the first embodiment. FIG. [Figure 11] 4 is a diagram showing another schematic configuration of the abnormality diagnostic device according to the first embodiment. FIG. [Figure 12] FIG. 12 is a schematic diagram of the circuit of FIG. [Figure 13] FIG. 2 is a diagram showing a schematic configuration of an abnormality diagnosis system according to a first modification of the first embodiment. [Figure 14] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis system according to a second modification of the first embodiment. [Figure 15] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis device according to a second embodiment. [Figure 16] 10 is a flowchart showing a processing flow of the abnormality check device according to the second embodiment. [Figure 17] 10 is a diagram showing an example of a frequency analysis result of a current signal measured by a current detection unit according to the second embodiment. FIG. [Figure 18] FIG. 10 is a diagram showing an example of sort data according to the second embodiment. [Figure 19] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis system according to a first modification of the second embodiment. [Figure 20] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis system according to a second modification of the second embodiment. [Figure 21] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnostic device according to a third embodiment. [Figure 22] 10 is a flowchart showing a processing flow of the abnormality check device according to the third embodiment. [Figure 23] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis device according to a fourth embodiment. [Figure 24] 10 is a flowchart showing a processing flow of the abnormality check device according to the fourth embodiment. [Figure 25] FIG. 10 is a diagram showing a schematic configuration of an abnormality diagnosis device according to a fifth embodiment. [Figure 26]10 is a flowchart showing a processing flow of the abnormality check device according to the fifth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Note that the drawings are schematic, and the relative sizes and positions of images shown in different drawings are not necessarily accurately depicted and may be changed as appropriate. In the following description, similar components are denoted by the same reference numerals, and their names and functions are assumed to be the same or similar. Therefore, detailed descriptions thereof may be omitted.

[0019] Embodiment 1 An abnormality diagnostic device 101 according to this embodiment will be described with reference to FIGS. In FIG. 1, the abnormality diagnostic device 101 includes a current detection unit 1 connected to one of wirings 9A, 9B, and 9C connected to an electric motor 5, a monitoring and diagnosing unit 2, and a diagnostic result output unit 3.

[0020] The current detection unit 1 measures the current flowing through the wiring 9A, 9B, and 9C, and thereby obtains the drive current that drives the electric motor 5. The current detection unit 1 outputs the obtained drive current as a current signal to the monitoring and diagnosing unit 2. The monitoring and diagnosing unit 2 determines whether there is an abnormality in the rotating machinery equipment 4. When the monitoring and diagnosing unit 2 determines there is an abnormality in the rotating machinery equipment 4, it sends the determination result to the diagnosing result output unit 3. The diagnosing result output unit 3 notifies the monitoring staff whether or not an abnormality has occurred.

[0021] The electric motor 5 is a three-phase AC motor, and is connected to a commercial power supply 8 via an inverter 7 and driven by the inverter 7. The inverter 7 is connected to a commercial power supply 8 and is configured by combining an AC-DC converter that converts AC power from the commercial power supply 8 into DC power and a DC-AC converter that converts the DC power into AC power, and supplies the AC power converted by the DC-AC converter to the electric motor 5. The rotating machinery equipment 4 has an electric motor 5 and a load equipment 6 connected to the electric motor 5 and powered by the electric motor 5. For example, the load equipment 6 is a water pump, a vacuum pump, a fan, a blower, or the like that is driven by the electric motor 5 as a power source.

[0022] As an example, we will explain the case where the abnormality diagnosis device 101 is applied to a public plant monitoring and control system, including water treatment plants such as water purification plants and sewage treatment plants. Load equipment 6, such as a water intake pump and a water supply pump, used in the water treatment plant is driven by an electric motor 5. The abnormality diagnosis device 101 performs abnormality diagnosis of the rotating machinery equipment 4, which is made up of the load equipment 6 and the electric motor 5, in a monitoring and diagnosing unit 2 using a current signal acquired from a current detecting unit 1 connected to one of the wirings 9A, 9B, and 9C connected to the electric motor 5. Any abnormality in the rotating machinery equipment 4 determined by the monitoring and diagnosing unit 2 is sent to a diagnosis result output unit 3, which notifies the operation management operator of the water treatment plant of the presence or absence of an abnormality.

[0023] In this embodiment, the rotating machinery equipment 4 has an electric motor 5 driven by an inverter 7 and a water pump which is a load equipment 6, and an example is described in which an abnormality caused by cavitation in the water pump accompanied by minute torque fluctuations is diagnosed by analyzing the current signal input from the current detection unit 1.

[0024] Cavitation is a phenomenon in which a liquid vaporizes and generates bubbles when the liquid is under low pressure. Furthermore, after bubbles have been generated, if the liquid is no longer under low pressure, the bubbles disappear with a large impact. Therefore, when cavitation occurs in a water pump, the bubbles obstruct the flow of liquid within the pump, reducing the pumping capacity of the water pump. Furthermore, when cavitation disappears within the water pump, the impact generated when the bubbles disappear can cause damage to the water pump, the formation of holes, and other abnormalities.

[0025] In this embodiment, an example has been shown in which the current detection unit 1 is connected to one of the commercial power supplies 8 connected to the electric motor 5, but the current detection unit 1 may be installed in each phase of the commercial power supply 8. Even in this case, it is sufficient to measure any one of the phases.

[0026] Although the present embodiment has been described as an example of detecting an abnormality caused by cavitation in a water pump that involves minute torque fluctuations, it is also possible to detect abnormalities caused by failure modes other than cavitation in a water pump that involve minute torque fluctuations. For example, failure modes other than cavitation in a water pump include foreign matter or air trapped in the water pump, accumulation of by-products in a vacuum pump, bearing wear, and missing blades in a fan.

[0027] 2, the monitoring and diagnosing unit 2 has a memory unit 21 and an analysis unit 22. The memory unit 21 has a current signal storage unit 21A, a judgment criterion storage unit 21B, and an abnormality judgment storage unit 21C. The analysis unit 22 has a frequency analysis unit 22A, a characteristic frequency band extraction unit 22B, a feature amount calculation unit 221C, and an abnormality diagnosis unit 22D. In FIG. 3, the diagnosis result output unit 3 has a display unit 31A, an alarm unit 31B, and an external output communication unit 31C.

[0028] 4 is a diagram showing the hardware configuration of the monitoring and diagnosing unit 2 in this embodiment. The monitoring and diagnosing unit 2 includes a transmitting / receiving device 23, a processor (CPU: Central Processing Unit) 24, a memory (ROM: Read Only Memory) 25, and a memory (RAM: Random Access Memory) 26. The monitoring and diagnosing unit 2 diagnoses abnormalities in the rotating machinery equipment 4 by the processor 24 processing a program stored in advance in the memory 25, and outputs the diagnosis results.

[0029] In the monitoring and diagnosing unit 2, various functional modules are realized by the processor 24 executing predetermined programs stored in the memory 25. The functional modules include an analysis unit 22. The memory 25 and the memory 26 include a memory unit 21. The transmitting and receiving device 23 transmits and receives signals to and from the current detection unit 1 connected to the monitoring and diagnosing unit 2 and the diagnosis result output unit 3 connected to the monitoring and diagnosing unit 2.

[0030] Each functional module of the monitoring and diagnostic unit 2 may be realized by the processor 24 executing software processing in accordance with a pre-set program as described above, or at least a portion of the functional modules may be configured to execute predetermined numerical and logical operation processing using hardware such as electronic circuits having functions corresponding to each functional module.

[0031] 5, the processing flow of the abnormality diagnosis device 101 in this embodiment will be described together with a detailed description of each component included in the abnormality diagnosis device 101. The flowchart consisting of the following steps is repeatedly executed every time a predetermined condition is met.

[0032] In step S1, the current detection unit 1 measures the current flowing through the electric motor 5 and outputs a current signal to the current signal storage unit 21A. The current signal storage unit 21A stores the current signal. 6 is a diagram showing the waveform of the current signal detected by the current detection unit 1. The vertical axis represents the current value, and the horizontal axis represents time. The waveform of the current signal is indicated by a dotted line (U phase), a dashed line (V phase), and a solid line (W phase).

[0033] Then, in step S2, the frequency analysis unit 22A performs frequency analysis on the waveform of the current signal acquired from the current signal storage unit 21A.

[0034] Figure 7 shows the results of frequency analysis of the waveform of the U-phase current signal indicated by the dotted line in Figure 6. The vertical axis represents the current power spectrum, and the horizontal axis represents frequency. The frequency analysis results shown in Figure 7 are for a power supply frequency of 50 Hz, and have spectrum peaks at 50 Hz, which is the power supply frequency, and 150 Hz, which is the third-order component of the power supply frequency. The power supply frequency is the frequency of the commercial power supply 8, and the third-order component of the power supply frequency is a frequency that is three times the power supply frequency. When a frequency has a frequency that is x times the power supply frequency, it is expressed as the xth-order component of the power supply frequency, where x is an integer greater than or equal to 0.

[0035] Here, the reason why the frequency analysis results shown in FIG. 7 have spectrum peaks at the power supply frequency and the third-order component of the power supply frequency will be explained. During the power conversion process by inverter 7, when AC power from commercial power source 8 is converted to DC power by the AC-DC converter, the operation of the converter circuit generates a current with a distorted waveform that is a combination of a fundamental wave with the power supply frequency and harmonics with frequencies that are integer multiples of the power supply frequency. This distorted current affects the voltage waveform, distorting it. A similarly distorted current flows through equipment to which a voltage with a distorted waveform is applied.

[0036] For these reasons, the frequency analysis result shown in Fig. 7 has spectral peaks at the power supply frequency and the third-order component of the power supply frequency. Furthermore, although the present embodiment has shown an example in which the waveform of the U-phase current signal is frequency analyzed, the current signals of other single phases, multiple phases, or all phases may also be frequency analyzed. Although FIG. 7 shows an example in which the power supply frequency is 50 Hz, the following description of the processing flow of the abnormality diagnostic device 101 will be given for the case in which the power supply frequency is 60 Hz.

[0037] 5, the characteristic frequency band extraction unit 22B extracts data belonging to the characteristic frequency band so as to include multiple spectral peaks within a predetermined frequency range. In this embodiment, the data extracted is data belonging to the characteristic frequency band so as to include multiple spectral peaks within a frequency range from the zeroth-order component to the second-order component of the power supply frequency. Specifically, from the data of the frequency analysis results of the U-phase current signal input from the frequency analysis unit 22A, the frequency range from 0 Hz, which is the zeroth component of the power supply frequency, to 120 Hz, which is the second-order component, is defined as a characteristic frequency band, and data belonging to this characteristic frequency band is extracted. Here, the characteristic frequency band refers to a frequency range used for calculating a characteristic amount, which will be described later.

[0038] Next, we will explain why the frequency range from the zeroth to second order components of the power supply frequency is defined as a characteristic frequency band and why data belonging to this characteristic frequency band is extracted. When cavitation occurs in load equipment 6, the torque of electric motor 5 required to drive load equipment 6 fluctuates slightly compared to when no cavitation occurs. Furthermore, since the torque of electric motor 5 is determined by the current value, minute fluctuations in the torque of electric motor 5 also affect the drive current of electric motor 5. As a result, the frequency analysis results of the current signal affected by minute torque fluctuations show that the signal strength increases in the frequency range from the zeroth-order component of the power supply frequency to the second-order component, which is the frequency range near the power supply frequency.

[0039] Figure 8 is an example of a graph comparing the results of frequency analysis of a current signal when minute torque fluctuations occur in the load equipment 6 and when they do not. The vertical axis represents the current power spectrum, and the horizontal axis represents frequency. A solid line represents when minute torque fluctuations occur, and a dotted line represents when minute torque fluctuations do not occur. The power supply frequency is 60 Hz, and the rotational frequency of the electric motor 5 is 30 Hz. The rotational frequency is the rotational speed of the electric motor 5 expressed as a frequency.

[0040] As shown by the solid line, it can be seen that the signal strength when minute torque fluctuations occur increases in the frequency range of the power supply frequency ±40 Hz on both the low-frequency and high-frequency sides, centered around 60 Hz, i.e., the frequency range of 20 Hz to 100 Hz, compared to when minute torque fluctuations do not occur. For this reason, the frequency range from 0 Hz, which is the zeroth-order component of the power supply frequency, to 120 Hz, which is the second-order component, is extracted as the characteristic frequency band.

[0041] Furthermore, while Fig. 8 shows the effect of minute torque fluctuations in the frequency range from the 0th to 2nd order components of the power supply frequency, which is the frequency range near the power supply frequency, the effect of minute torque fluctuations is also seen in the frequency range from the 4th to 6th order components of the power supply frequency, which is the frequency range near the 5th order component of the power supply frequency, and in the frequency range from the 6th to 8th order components of the power supply frequency, which is the frequency range near the 7th order component of the power supply frequency.

[0042] In addition, as shown in Fig. 8, multiple spectral peaks appear on both sides of the power supply frequency within the frequency range from the zeroth-order component to the second-order component of the power supply frequency. In Fig. 8, spectrum 12A of the sideband components of the modulated wave, which will be described later, appears as spectral peaks at frequencies between the power supply frequency and the rotational frequency, i.e., 30 Hz and 90 Hz. Furthermore, spectrum 12B of the noise components caused by the switching operation of inverter 7, which will be described later, appears as spectral peaks at frequencies between the power supply frequency and the rotational frequency, i.e., 40 Hz and 80 Hz.

[0043] A modulated wave is one of the elements used in PWM (Pulse Width Modulation) control, which is a power control method for DC-AC inverters. Here, the modulated wave is a fundamental wave, and the modulated wave frequency, which is the frequency of the modulated wave, corresponds to the power supply frequency. The sideband components of the modulated wave depend on the rotational frequency of the motor 5, and appear at frequencies shifted by the rotational frequency on both sides of the modulated wave frequency. In Fig. 8, spectrum 12A of the sideband components of the modulated wave appears at frequencies between the power supply frequency and the rotational frequency.

[0044] In the power conversion process by inverter 7, when DC power is converted to AC power by the DC-AC inverter, the DC voltage output from the AC-DC converter and the AC voltage output to motor 5 fluctuate slightly at the power supply frequency and an integer multiple of the power supply frequency due to the switching operation of the inverter circuit. This slightly fluctuating value appears as spectrum 12B of the noise component caused by the switching operation of inverter 7. In FIG. 8, the noise component spectrum 12B appears at the power supply frequency ±20 Hz, but depending on the control method and type of inverter 7 used, it may also appear at frequencies other than the power supply frequency ±20 Hz.

[0045] The spectrum 12A of the sideband wave component of the modulated wave and the spectrum 12B of the noise component caused by the switching operation of the inverter 7 are examples of noise caused by the influence of inverter driving. In addition, an example has been shown in which the spectrum 12A of the sideband wave component and the spectrum 12B of the noise component appear within the frequency range from the zeroth component to the second component of the power supply frequency, but if an abnormality other than cavitation occurs at the same time, a spectral peak different from the spectrum 12A of the sideband wave component and the spectrum 12B of the noise component may appear within the frequency range.

[0046] Although the example has been given in which data belonging to a characteristic frequency band is extracted so as to include multiple spectral peaks within the frequency range from the 0th to 2nd order components of the power supply frequency, the present invention is not limited to this. Data belonging to a characteristic frequency band may be extracted so as to include multiple spectral peaks within the frequency range from the 4th to 6th order components of the power supply frequency, the frequency range from the 6th to 8th order components of the power supply frequency, etc. For example, data belonging to a characteristic frequency band may be extracted so as to include multiple spectral peaks within a frequency range such as 0 Hz to 120 Hz, a frequency range such as 200 Hz to 360 Hz, or a frequency range such as 300 Hz to 480 Hz, to accommodate both a power supply frequency of 50 Hz and a power supply frequency of 60 Hz.

[0047] FIG. 9 is a diagram showing a case where data belonging to a characteristic frequency band, that is, a frequency range of 0 to 120 Hz, is extracted from the graph comparing the results of frequency analysis of the current signals shown in FIG. In step S3 of FIG. 5, the characteristic frequency band extraction unit 22B extracts data belonging to a characteristic frequency band as shown in FIG. 9 from the data resulting from the frequency analysis of the U-phase current signal input from the frequency analysis unit 22A.

[0048] In step S31 of FIG. 5, the feature calculation unit 221C detects and excludes the spectrum of the power supply frequency, the spectrum of the secondary component of the power supply frequency, the spectrum 12A of the sideband wave component, and the spectrum 12B of the noise component as spectral peaks from the data belonging to the feature frequency band input from the feature frequency band extraction unit 22B.

[0049] For example, one method of detecting spectral peaks is to detect a predetermined number of data items with the highest signal strengths from among data items belonging to a characteristic frequency band as spectral peaks.Other methods include dividing the characteristic frequency band at predetermined frequency intervals, calculating the average value of each signal strength within each divided range, comparing each signal strength within each divided range with the average value, and detecting data items with signal strengths exceeding the average value as spectral peaks.

[0050] When step S31 is applied to data belonging to the characteristic frequency band in FIG. 9, the spectrum of the power supply frequency, the spectrum of the second-order component of the power supply frequency, the spectrum 12A of the sideband wave component, and the spectrum 12B of the noise component are detected as spectral peaks from the data belonging to the characteristic frequency band and are excluded.

[0051] Furthermore, in the present embodiment, an example has been shown in which the sideband wave component spectrum 12A and the noise component spectrum 12B are excluded from the data belonging to the characteristic frequency band, but if a spectral peak caused by factors other than the sideband wave component spectrum 12A and the noise component spectrum 12B appears in the characteristic frequency band, the spectral peak caused by factors other than the sideband wave component spectrum 12A and the noise component spectrum 12B is also excluded. In other words, the spectral peak appearing in the characteristic frequency band is excluded so as not to be used in diagnosing an abnormality due to a failure mode accompanied by a minute torque fluctuation.

[0052] Here, the reason why spectral peaks are excluded from the data belonging to the characteristic frequency band in step S31 of FIG. 5 will be explained. When minute torque fluctuations occur, as shown in FIG. 8, the frequency characteristics of the current signal affected by the minute torque fluctuations show an increase in signal strength in the frequency range of the power supply frequency ±40 Hz. On the other hand, because the current change due to the minute torque fluctuation is minute, the increase in signal strength in the characteristic frequency band is also minute compared to the spectral peaks. Here, if spectral peaks are included in the data belonging to the characteristic frequency band, there is a risk that the minute changes in signal strength will be buried by the spectral peaks. For this reason, spectral peaks are excluded from the data belonging to the characteristic frequency band in step S31.

[0053] 5, the feature calculation unit 221C calculates a feature from the data belonging to the characteristic frequency band excluding the spectral peaks. The feature can be calculated by using the data belonging to the characteristic frequency band excluding the spectral peaks and calculating the sum of all signal intensities included in this data. That is, in this embodiment, the feature is the sum of all signal intensities included in the data belonging to the characteristic frequency band excluding the spectral peaks.

[0054] Here, the signal intensity refers to a current value or a current power spectrum. In Fig. 9, the feature amount corresponds to the sum of all current power spectra included in the data belonging to the feature frequency band, excluding the spectral peak.

[0055] Then, in step S4A of FIG. 5, the feature calculation unit 221C determines whether the initial learning record period T is shorter than the predetermined period T0, where T is the initial learning record period and T0 is the predetermined period.

[0056] Here, the initial learning record refers to calculating feature values ​​in step S41 and storing the feature values ​​in the judgment criterion memory unit 21B during a predetermined period from the start of operation of the rotating machinery equipment 4, and the initial learning record period refers to a predetermined period during which the initial learning record is repeatedly executed in order to generate the judgment criterion described below.

[0057] If the initial learning record period T is shorter than the predetermined period T0 (step S4A: YES), the process proceeds to step S4B, and if the initial learning record period T is equal to or longer than the predetermined period T0 (step S4A: NO), the process proceeds to step S4E. Specifically, the initial learning record period T is shorter than the predetermined period T0 refers to the period from the start of operation of the rotating machinery equipment 4 to the initial learning record period, and the initial learning record period T is equal to or longer than the predetermined period T0 refers to the period after the end of the initial learning record period during which the abnormality diagnosis device 101 performs abnormality diagnosis on the rotating machinery equipment 4.

[0058] Then, in step S4B, the feature amount calculation unit 221C accumulates the feature amounts in the determination criterion storage unit 21B and performs initial learning recording. Then, in step S4C, the feature calculation unit 221C determines whether the initial learning record period T is shorter than a predetermined period T0. If the initial learning record period T is shorter than the predetermined period T0 (step S4C: YES), the process proceeds to step S1. If the initial learning record period T is equal to or longer than the predetermined period T0 (step S4C: NO), the process proceeds to step S4D.

[0059] Then, in step S4D, the feature calculation unit 221C generates a judgment criterion, which is a first threshold value, by performing statistical processing on the feature accumulated in the judgment criterion memory unit 21B during a predetermined period T0 from the start of operation of the rotating machinery equipment 4 based on the initial learning record, and stores the generated judgment criterion in the judgment criterion memory unit 21B. For example, a statistical processing method for generating a criterion is a method for generating the criterion by calculating the average, variations σ, 2σ, 3σ, etc. of the feature amounts stored in the criterion storage unit 21B.

[0060] Then, in step S4E, the abnormality diagnosis unit 22D determines whether the feature amount is equal to or greater than the first threshold value. If the feature amount is equal to or greater than the first threshold value (step S4E: YES), the process proceeds to step S5. If the feature amount is smaller than the first threshold value (step S4E: NO), the process proceeds to step S7.

[0061] Then, in step S5, the abnormality diagnosing unit 22D diagnoses that the rotating machinery equipment 4 is abnormal, and outputs the diagnosis result to the abnormality determination storage unit 21C.

[0062] Here, if the number of data items in the criterion generated by initial learning differs from the number of data items in the feature quantities, the feature quantity calculation unit 221C cannot accurately compare the criterion with the feature quantities, so it performs a process to align the number of data items in the criterion with the number of data items in the feature quantities. If the number of data items in the criterion is greater than the number of data items in the feature quantities, data with higher signal strengths is removed from the criterion data in order to align the number of data items in the criterion with the number of data items in the feature quantities. If the number of data items in the criterion is less than the number of data items in the feature quantities, data with higher signal strengths is removed from the feature quantities in order to align the number of data items in the feature quantities with the number of data items in the criterion.

[0063] For example, the number of data points in the judgment criteria may differ from the number of data points in the feature amounts when the resolution of the frequency analysis is the same but the frequency range of the feature frequency band when extracting data belonging to the feature frequency band is different.

[0064] Then, in step S6, the diagnostic result output unit 3 acquires the diagnostic result from the abnormality determination storage unit 21C. As a result, the diagnostic result output unit 3 displays the diagnostic result on a display unit 31A such as a display, the alarm unit 31B issues an alarm by sounding an alarm or turning on or blinking an abnormality lamp when the rotating machinery equipment 4 is diagnosed as abnormal, and the external output communication unit 31C transmits the diagnostic result to an external device such as a control panel, a PC (Personal Computer), or a cloud server.

[0065] Then, in step S7, the abnormality diagnosis unit 22D determines whether or not to continue the abnormality diagnosis. If the abnormality diagnosis is to be continued (step S7: YES), the process proceeds to step S1, and if the abnormality diagnosis is not to be continued (step S7: NO), the process ends.

[0066] For example, a method of determining whether to continue the abnormality diagnosis may include setting a period for continuing the abnormality diagnosis in the abnormality diagnosis unit 22D in advance, and determining not to continue the abnormality diagnosis if the period for continuing the abnormality diagnosis has expired. For example, the period for continuing the abnormality diagnosis may be a period during which the rotating machinery equipment 4 is in operation. In this way, abnormalities in the rotating machinery equipment 4 are diagnosed through steps S1 to S7 in FIG.

[0067] In this way, the abnormality diagnosis device 101 of this embodiment performs abnormality diagnosis of the rotating machinery equipment 4 by comparing the sum of all signal strengths included in the data belonging to the characteristic frequency band, excluding the data detected as the spectral peak, with the first threshold value. This prevents minute changes in signal strength due to minute torque fluctuations from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations, and as a result, making it possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.

[0068] As described above, the abnormality diagnosis device 101 of this embodiment is an abnormality diagnosis device 101 that diagnoses abnormalities in the rotating machinery equipment 4, and includes: a current signal memory unit 21A that stores the current signal of the electric motor 5; a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A; a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal; a feature calculation unit 221C that detects multiple spectral peaks from the data belonging to the feature frequency band, excludes data detected as multiple spectral peaks from the data belonging to the feature frequency band, and calculates the sum of signal intensities included in the data belonging to the feature frequency band excluding the data detected as the multiple spectral peaks; and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than a first threshold value. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be accurately diagnosed.

[0069] As described above, the abnormality diagnosis method of this embodiment includes a current signal detection step S1 for detecting a current signal flowing through the electric motor 5, a frequency analysis step S2 for performing frequency analysis on the waveform of the current signal detected in the current signal detection step S1, a characteristic frequency band extraction step S3 for extracting data belonging to a characteristic frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step S2, a data exclusion step S31 for detecting multiple spectral peaks from the data belonging to the characteristic frequency band and excluding data detected as the multiple spectral peaks from the data belonging to the characteristic frequency band, a feature calculation step S41 for calculating the sum of signal intensities included in the data belonging to the characteristic frequency band from which the data detected as the multiple spectral peaks have been excluded, a determination step S4E for determining whether the sum is equal to or greater than a first threshold, and an abnormality diagnosis step S5 for diagnosing an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be accurately diagnosed.

[0070] As described above, the program of this embodiment is a program for diagnosing an abnormality in the rotating machinery equipment 4, and is characterized by causing a computer to execute the following: a current signal detection step S1 for detecting a current signal flowing through the electric motor 5; a frequency analysis step S2 for performing frequency analysis on the waveform of the current signal detected in the current signal detection step S1; a feature frequency band extraction step S3 for extracting data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result, which is the result of frequency analysis of the waveform of the current signal in the frequency analysis step S2; a data exclusion step S31 for detecting multiple spectral peaks from the data belonging to the feature frequency band and excluding data detected as multiple spectral peaks from the data belonging to the feature frequency band; a feature calculation step S41 for calculating the sum of signal intensities included in the data belonging to the feature frequency band from which the data detected as multiple spectral peaks have been excluded; a determination step S4E for determining whether the sum is equal to or greater than a first threshold; and an abnormality diagnosis step S5 for diagnosing an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be accurately diagnosed.

[0071] In this embodiment, an example has been shown in which the electric motor 5 is driven by the inverter 7, but as shown in FIG. 10 , the electric motor 5 may be connected to a commercial power supply 8 via a plurality of molded case circuit breakers 10A, 10B, and 10C and a plurality of electromagnetic contactors 11A, 11B, and 11C, and may be driven by the commercial power supply 8.

[0072] In addition, in this embodiment, an example has been shown in which the abnormality diagnosis device 101 has a current detection unit 1, a monitoring and diagnosis unit 2, and a diagnosis result output unit 3, but the current detection unit 1 and the diagnosis result output unit 3 may be provided in an external device different from the abnormality diagnosis device 101.

[0073] 11, the abnormality diagnosis device 101 utilizes a current detection unit 71 that detects a current flowing through an AC bus of the electric motor 5 for inverter control, and an inverter control device 72 that is configured with a processor (CPU) and a memory, both of which are built into the inverter 7, and at least one of the current detection unit 1, the monitoring and diagnosing unit 2, and the diagnosis result output unit 3 may be implemented in the inverter 7. FIG. 12 is a schematic diagram showing the functional configuration diagram of FIG. 11 in the form of a circuit. FIG. 12 shows an example in which the current detection unit 1, the monitoring and diagnosing unit 2, and the diagnosis result output unit 3 are all implemented in the inverter 7.

[0074] The hardware configuration of the inverter control device 72 is similar to that of the monitoring and diagnosing unit 2 shown in Fig. 4. It includes a transmitter / receiver 23, a processor (CPU: Central Processing Unit) 24, a memory (ROM: Read Only Memory) 25, and a memory (RAM: Random Access Memory) 26. The processor 24 processes a program pre-stored in the memory 25. The inverter control device 72 performs a three-phase / dq transformation based on information about the current flowing through the electric motor 5 based on the output result of the current detection unit 71 and the motor angle detected by a rotation angle sensor (not shown) provided in the electric motor 5. The detected d-axis and q-axis current values ​​are stored in the memory 26, and these values ​​are compared with a d-axis current command value and a q-axis current command value provided by a higher-level control device to calculate a d-axis voltage command value and a q-axis voltage command value. The calculated d-axis and q-axis voltage command values ​​and rotational position information of the electric motor 5 are converted from two phases to three phases, and the transmitter / receiver 23 outputs a voltage command value to the windings of the electric motor 5. By utilizing the processor 24 and memories 25 and 26 of such an inverter control device 72, it is possible to provide the inverter control device 72 with a monitoring and diagnosing unit 2 and a diagnosing result output unit 3. One phase of the current detection unit 71 is used as the current detection unit 1, a predetermined program is stored in the memory 25, and the processor 24 executes this program, thereby realizing various functional modules of the monitoring and diagnosing unit 2.

[0075] Furthermore, in the present embodiment, an example has been shown in which data belonging to a characteristic frequency band is extracted so as to include multiple spectral peaks within a frequency range from the zeroth-order component to the second-order component of the power supply frequency, and the extracted data belonging to the characteristic frequency band is used to perform abnormality diagnosis of the rotating machinery equipment. However, data belonging to characteristic frequency bands may be extracted so as to include multiple spectral peaks within multiple frequency ranges, and the extracted data belonging to the multiple characteristic frequency bands may be used to perform abnormality diagnosis of the rotating machinery equipment. For example, data belonging to characteristic frequencies that include multiple spectral peaks may be extracted from the frequency range of the zeroth to second order components of the power supply frequency and the frequency range of the fourth to sixth order components of the power supply frequency, and abnormality diagnosis of rotating machinery equipment may be performed using the data belonging to the characteristic frequency band extracted from the frequency range of the zeroth to second order components of the power supply frequency and the characteristic frequency band extracted from the frequency range of the fourth to sixth order components of the power supply frequency.

[0076] In addition, in the present embodiment, an example has been shown in which the judgment criteria used for abnormality diagnosis are generated by repeatedly performing an initial learning record in which calculated feature quantities are accumulated in the judgment criterion storage unit 21B for a predetermined period from the start of operation of the rotating machinery equipment, and by performing statistical processing on the accumulated feature quantities. However, predetermined judgment criteria may also be set and stored in the judgment criterion storage unit 21B in advance.

[0077] <Variation 1> FIG. 13 is a diagram illustrating an abnormality diagnosis system 201 according to a first modification of the first embodiment. The configuration example shown in FIG. 1 includes an abnormality diagnosis device 101 in which a current detection unit 1, a monitoring and diagnosing unit 2, and a diagnosis result output unit 3 are integrated, and the abnormality diagnosis device 101 performs abnormality diagnosis of rotating machinery equipment 4. However, the abnormality diagnosis system 201 shown in FIG. 13 includes a server 30 including the monitoring and diagnosing unit 2 and the diagnosis result output unit 3, and current detection units 1-1 to 1-n connected to rotating machinery equipment 4-1 to 4-n including electric motors 5-1 to 5-n and load equipment 6-1 to 6-n, and the current detection units 1-1 to 1-n are connected to the server 30 via a network. n is the number of rotating machinery equipment 4-1 to 4-n and is an integer equal to or greater than 1. The current detection units 1-1 to 1-n measure the current signals of the corresponding rotating machinery equipment 4-1 to 4-n. In this case, the monitoring and diagnosing unit 2 of the abnormality diagnosis system 201 acquires current signals from the current detecting units 1-1 to 1-n corresponding to the rotating machinery equipment 4-1 to 4-n via a network. Other than this, the operation and configuration of the abnormality diagnosis system 201 are the same as the example shown in the first embodiment.

[0078] In this manner, the abnormality diagnosis system 201 of the modified example shown in FIG. 13 has the effect of eliminating the need to provide the abnormality diagnosis device 101 in each of the rotating machinery equipment 4-1 to 4-n. The electric motors 5-1 to 5-n may be of the same model, or at least some of them may be of a model different from the other electric motors 5-1 to 5-n. The load equipment 6-1 to 6-n may be of the same type, or at least some of them may be of a different type from the other load equipment 6-1 to 6-n.

[0079] As described above, the abnormality diagnosis system 201 of this embodiment is an abnormality diagnosis system 201 that diagnoses abnormalities in rotating machinery equipment 4, and includes a current signal memory unit 21A that stores the current signal of the electric motor 5, a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A, a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal, a feature calculation unit 221C that detects multiple spectral peaks from the data belonging to the feature frequency band, excludes data detected as multiple spectral peaks from the data belonging to the feature frequency band, and calculates the sum of signal intensities included in the data belonging to the feature frequency band excluding the data detected as the multiple spectral peaks, and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is greater than or equal to a first threshold value. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectrum peak, the detection accuracy of minute torque fluctuations is improved, and as a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be diagnosed with high accuracy.In addition, there is an effect that it is not necessary to provide the abnormality diagnosis device 101 in each of the rotating machinery equipment 4-1 to 4-n.

[0080] <Variation 2> 14 is a diagram showing an abnormality diagnosis system 202 according to Modification 2 of Embodiment 1. In the configuration example shown in FIG. 1, an abnormality diagnosis device 101 is provided in which a current detection unit 1, a monitoring and diagnosing unit 2, and a diagnosis result output unit 3 are integrated, and the abnormality diagnosis device 101 performs abnormality diagnosis of rotating machinery equipment 4. 14The fault diagnosis system 202 shown in FIG. 1 includes fault diagnosis devices 202-1 to 202-n including current detection units 1-1 to 1-n and monitoring and diagnosing units 2-1 to 2-n connected to rotating machine equipment 4-1 to 4-n including electric motors 5-1 to 5-n and load equipment 6-1 to 6-n, and a server 40 including a data acquisition unit 42 and a diagnosis result output unit 3, and the fault diagnosis devices 202-1 to 202-n and the server 40 are connected via a network. In this case, the fault diagnosis devices 202-1 to 202-n transmit their diagnosis results via the network, and the diagnosis result output unit 3 of the server 40 acquires the diagnosis results from the fault diagnosis devices 202-1 to 202-n via the network. Other than this, the operation and configuration of the fault diagnosis system 202 are the same as those of the example shown in the first embodiment.

[0081] In this way, the abnormality diagnosis system 202 of this modified example shown in Figure 14 can display the diagnosis results of the rotating machinery equipment 4-1 to 4-n all at once, making it easy to compare the rotating machinery equipment 4-1 to 4-n and manage the entire system.

[0082] As described above, the abnormality diagnosis system 202 of this embodiment is an abnormality diagnosis system 202 that diagnoses abnormalities in the rotating machinery equipment 4, and includes: a current signal memory unit 21A that stores the current signal of the electric motor 5; a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A; a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal; a feature calculation unit 221C that detects multiple spectral peaks from the data belonging to the feature frequency band, excludes data detected as multiple spectral peaks from the data belonging to the feature frequency band, and calculates the sum of signal intensities included in the data belonging to the feature frequency band excluding the data detected as the multiple spectral peaks; and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is greater than or equal to a first threshold value. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectrum peaks, improving the accuracy of detecting minute torque fluctuations, and as a result, it is possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.In addition, the diagnosis results of the rotating machinery equipment 4-1 to 4-n can be displayed all at once, making it easy to compare the rotating machinery equipment 4-1 to 4-n and manage the entire equipment.

[0083] Embodiment 2 The abnormality diagnostic device 102 according to this embodiment will be described with reference to FIGS. In the first embodiment, a configuration has been described in which an abnormality diagnosis of the rotating machinery equipment 4 is performed by comparing the sum of all signal intensities included in data belonging to a characteristic frequency band, excluding spectral peaks, with a first threshold value, but in the present embodiment, sorted data is created by rearranging data belonging to the characteristic frequency band in descending order of signal intensity, and data whose signal intensity is equal to or greater than a predetermined second threshold value is excluded from the characteristic frequency band. The other configurations are the same as in the first embodiment, and the same reference numerals are used to refer to the same or corresponding parts as in the first embodiment.

[0084] The abnormality diagnostic device 102 of this embodiment differs from the abnormality diagnostic device 101 of the first embodiment in that the monitoring and diagnosing unit 2 includes a feature amount calculation unit 222C instead of the feature amount calculation unit 221C, as shown in FIG. 16, the processing flow of the abnormality diagnosis device 102 in this embodiment will be described together with a detailed description of each component included in the abnormality diagnosis device 102. The processing other than steps S32 and S42 is the same as in the first embodiment.

[0085] In step S32, the feature calculation unit 222C creates sorted data by rearranging the data belonging to the feature frequency band extracted by the feature frequency band extraction unit 22B in order of signal strength, and excludes data whose signal strength is equal to or greater than a predetermined second threshold from the feature frequency band. For example, the second threshold value may be determined by determining the lowest signal strength among the data belonging to the characteristic frequency band, and setting the value of the lowest signal strength +10 dB as the second threshold value.

[0086] A specific example of step S32 will be shown with reference to FIGS. 17 is a diagram showing a case where data included in a frequency range of the power supply frequency ±20 Hz, i.e., a frequency range of 40 Hz to 80 Hz, is extracted as data belonging to a characteristic frequency band from the graph comparing the frequency analysis results of the current signal shown in FIG. 7. The vertical axis represents the current power spectrum, which is a type of signal strength, and the horizontal axis represents frequency. When the frequency analysis unit 22A performs frequency analysis on the current signal with a resolution of 0.25 Hz in step S2, the number of data items belonging to the characteristic frequency band is 160, and the 160 pieces of data are sorted in order of signal strength. For example, when the above-described method for determining the second threshold is used, if the lowest signal strength in FIG. 17 is determined to be −60 dB, the second threshold will be −50 dB. FIG. 18 is a diagram showing sorted data in which 160 pieces of data in the characteristic frequency band shown in FIG. 17 are sorted in order of signal strength from 0 on the left side of the page to 160 on the right side of the page. The vertical axis represents the current power spectrum, which is a type of signal strength, and the horizontal axis represents the data rank. In FIG. 18, as shown by the solid line, the data rank is assigned in order of signal strength. In addition, data with a signal strength (current power spectrum) higher than −50 dB, which is a second threshold set in advance, is excluded from the sorted data.

[0087] 16, the feature calculation unit 222C calculates a feature from the data belonging to the characteristic frequency band, excluding data whose signal strength is higher than the second threshold. In this embodiment, the feature corresponds to the sum of all signal strengths included in the data belonging to the characteristic frequency band, excluding data whose signal strength is equal to or higher than the second threshold.

[0088] In this way, the abnormality diagnosis device 102 of this embodiment creates sorted data by rearranging data belonging to a characteristic frequency band in descending order of signal strength, and excludes data whose signal strength is equal to or greater than a predetermined second threshold from the data belonging to the characteristic frequency band. The sum of all signal strengths included in the data belonging to the characteristic frequency band, excluding data equal to or greater than the second threshold, is compared with the first threshold to perform abnormality diagnosis of the rotating machinery equipment 4. This prevents minute changes in signal strength due to minute torque fluctuations from being buried in spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, it is possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.

[0089] As described above, the abnormality diagnosis device 102 of this embodiment is an abnormality diagnosis device 102 that diagnoses abnormalities in the rotating machinery equipment 4, and includes: a current signal memory unit 21A that stores the current signal of the electric motor 5; a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A; a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal; a feature calculation unit 222C that rearranges the data belonging to the feature frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band, and calculates the sum of signal strengths included in the data belonging to the feature frequency band excluding the data whose signal strength is equal to or greater than the second threshold; and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be accurately diagnosed.

[0090] As described above, the abnormality diagnosis method of this embodiment is an abnormality diagnosis method for diagnosing an abnormality in the rotating machinery equipment 4, and includes: a current signal detection step S1 for detecting a current signal flowing in the electric motor 5; a frequency analysis step S2 for performing frequency analysis on the waveform of the current signal detected in the current signal detection step S1; a characteristic frequency band extraction step S3 for extracting data belonging to a characteristic frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step S2; a data exclusion step S32 for sorting the data belonging to the characteristic frequency band in order of signal strength and excluding data whose signal strength is equal to or greater than a second threshold from the data belonging to the characteristic frequency band; a feature calculation step S42 for calculating the sum of signal strengths included in the data belonging to the characteristic frequency band from which the data whose signal strength is equal to or greater than the second threshold has been excluded; a determination step S4E for determining whether the sum is equal to or greater than a first threshold; and an abnormality diagnosis step S5 for diagnosing an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectral peaks, improving the accuracy of detecting minute torque fluctuations. As a result, abnormalities due to failure modes accompanied by minute torque fluctuations can be accurately diagnosed.

[0091] As described above, the program of this embodiment is a program for diagnosing an abnormality in the rotating machinery equipment 4, and includes the following steps in a computer: a current signal detection step S1 for detecting a current signal flowing through the electric motor 5; a frequency analysis step S2 for performing frequency analysis on the waveform of the current signal detected in the current signal detection step S1; and a characteristic frequency band extraction step S3 for extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from the frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step S2. a data excluding step of sorting the data belonging to the characteristic frequency band in order of signal strength and excluding data having a signal strength equal to or greater than a second threshold from the data belonging to the characteristic frequency band; S32 and Data with signal strength above the second thresholdThe method is characterized by executing a feature amount calculation step S42 of calculating the sum of signal strengths included in data belonging to the excluded feature frequency band, a determination step S4E of determining whether the sum is equal to or greater than a first threshold, and an abnormality diagnosis step S5 of diagnosing an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute signal strength changes due to minute torque fluctuations are prevented from being buried in spectral peaks, improving the detection accuracy of minute torque fluctuations, and as a result, it is possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.

[0092] <Variation 1> An abnormality diagnosis system 203 according to a first modification of the second embodiment will be described with reference to FIG. In the first modification of the first embodiment, a configuration including a server 30 including a monitoring and diagnosing unit 2 including a feature amount calculation unit 221C and a diagnosis result output unit 3, and current detection units 1-1 to 1-n connected to rotating mechanical equipment 4-1 to 4-n including electric motors 5-1 to 5-n and load equipment 6-1 to 6-n has been described with reference to Fig. 13, but this modification differs from the first modification of the first embodiment in that the monitoring and diagnosing unit 2 is the monitoring and diagnosing unit 2 according to the second embodiment. The rest of the configuration is the same as in the first modification of the first embodiment, and the same reference numerals are used to designate the same or corresponding parts as in the first modification of the first embodiment.

[0093] In this way, the abnormality diagnosis system 203 of this modified example shown in FIG. 19 has the effect of eliminating the need to provide the abnormality diagnosis device 102 in each of the rotating machinery equipment 4-1 to 4-n.

[0094] As described above, the abnormality diagnosis system 203 of this embodiment is an abnormality diagnosis system 203 that diagnoses abnormalities in the rotating machinery equipment 4, and includes: a current signal memory unit 21A that stores the current signal of the electric motor 5; a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A; a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal; a feature calculation unit 222C that rearranges the data belonging to the feature frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band, and calculates the sum of signal strengths included in the data belonging to the feature frequency band excluding the data whose signal strength is equal to or greater than the second threshold; and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectrum peaks, improving the accuracy of detecting minute torque fluctuations, and as a result, it is possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.In addition, there is an effect that it is not necessary to provide an abnormality diagnosis device 102 for each of the rotating machinery equipment 4-1 to 4-n.Furthermore, it is possible to collectively display the diagnosis results of the rotating machinery equipment 4-1 to 4-n, facilitating comparison between the rotating machinery equipment 4-1 to 4-n and overall management.

[0095] <Variation 2> An abnormality diagnosis system 204 according to the second modification of the embodiment will be described with reference to FIG. In the second modification of the first embodiment, a configuration including current detection units 1-1 to 1-n connected to rotating machine equipment 4-1 to 4-n including electric motors 5-1 to 5-n and load equipment 6-1 to 6-n, and abnormality diagnosis devices 202-1 to 202-n including monitoring and diagnosing units 2-1 to 2-n, and server 40 including data acquisition unit 42 and diagnosis result output unit 3 was described with reference to Fig. 14, but in this modification, the abnormality diagnosis devices are abnormality diagnosis devices 204-1 to 204-n including current detection units 1-1 to 1-n and monitoring and diagnosing units 2-1 to 2-n according to the second embodiment, which differs from the second modification of the first embodiment in that the other configurations are the same as those in the second modification of the first embodiment, and the same reference numerals are used to refer to the same or corresponding components as those in the second modification of the first embodiment.

[0096] In this way, the abnormality diagnosis system 204 of this modified example shown in Figure 20 can display the diagnosis results of the rotating machinery equipment 4-1 to 4-n all at once, making it easy to compare the rotating machinery equipment 4-1 to 4-n and manage the entire system.

[0097] As described above, the abnormality diagnosis system 204 of this embodiment is an abnormality diagnosis system 204 that diagnoses abnormalities in the rotating machinery equipment 4, and includes: a current signal memory unit 21A that stores the current signal of the electric motor 5; a frequency analysis unit 22A that performs frequency analysis on the waveform of the current signal stored in the current signal memory unit 21A; a feature frequency band extraction unit 22B that extracts data belonging to a feature frequency band so as to include multiple spectral peaks within a predetermined frequency range from the frequency analysis result obtained by the frequency analysis unit 22A of the waveform of the current signal; a feature calculation unit 222C that rearranges the data belonging to the feature frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the feature frequency band, and calculates the sum of signal strengths included in the data belonging to the feature frequency band excluding the data whose signal strength is equal to or greater than the second threshold; and an abnormality diagnosis unit 22D that diagnoses an abnormality in the rotating machinery equipment 4 if the sum is equal to or greater than the first threshold. In this way, minute changes in signal strength due to minute torque fluctuations are prevented from being buried in the spectrum peaks, improving the accuracy of detecting minute torque fluctuations, and as a result, it is possible to accurately diagnose abnormalities due to failure modes accompanied by minute torque fluctuations.In addition, the diagnosis results of the rotating machinery equipment 4-1 to 4-n can be displayed all at once, making it easy to compare the rotating machinery equipment 4-1 to 4-n and manage the entire equipment.

[0098] Embodiment 3 The abnormality diagnostic device 103 according to this embodiment will be described with reference to FIGS. In the first or second embodiment, a configuration for accurately diagnosing an abnormality due to a failure mode accompanied by a minute torque fluctuation has been described. However, the present embodiment differs from the first or second embodiment in that an abnormality due to a failure mode accompanied by a minute torque fluctuation is accurately diagnosed and the total operating time during which an abnormality occurs in the rotating machinery equipment 4 is calculated. The other configuration is the same as in the first or second embodiment, but as an example, a case will be described in which the same feature amount calculation unit 222C as in the second embodiment is included. The same reference numerals are used to refer to the same or equivalent components as in the first or second embodiment.

[0099] The abnormality diagnosis device 103 of this embodiment differs from the abnormality diagnosis device 101 of embodiment 1 or the abnormality diagnosis device 102 of embodiment 2 in that the monitoring and diagnosing unit 2 further includes an abnormality index storage unit 213D and an abnormality index calculation unit 223E, as shown in FIG. 21 . 22, the processing flow of the abnormality diagnosis device 103 in this embodiment will be described together with a detailed description of each component included in the abnormality diagnosis device 103. The processing other than step S5A, step S5B, and step S63 is the same as in embodiment 1 or 2, and the same reference numerals are used to denote the same or equivalent parts as in embodiment 1 or 2.

[0100] In step S5A, abnormality determination storage unit 21C accumulates the diagnosis results output from abnormality diagnosing unit 22D. Then, in step S5B, the abnormality index calculation unit 223E uses the abnormality diagnosis result obtained from the abnormality judgment memory unit 21C to calculate the total operating time during which an abnormality occurs in the rotating machinery equipment 4 as the abnormality cumulative time, and outputs the abnormality cumulative time to the abnormality index memory unit 213D.

[0101] Then, in step S63, the diagnosis result output unit 3 acquires the accumulated abnormality time from the abnormality index storage unit 213D. The diagnosis result output unit 3 displays the accumulated abnormality time on the display unit 31A such as a display in addition to the diagnosis result acquired from the abnormality determination storage unit 21C in the first or second embodiment, and the external output communication unit 31C outputs the accumulated abnormality time to an external device such as a control device panel, a PC, or a cloud server, thereby notifying the monitoring staff of the accumulated abnormality time and monitoring the trend of the accumulated abnormality time.

[0102] As described above, in addition to the effects of the first or second embodiment, the abnormality diagnosis device 103 of this embodiment calculates the total operating time during which an abnormality occurs in the rotating machinery equipment 4, and thereby notifies the monitoring staff of the accumulated abnormality time during which an abnormality occurred in the rotating machinery equipment 4 along with the abnormality diagnosis result, thereby making it possible to monitor trends in the accumulated abnormality time. In addition, by comparing the accumulated abnormality times of multiple rotating machinery equipment 4, the accumulated abnormality time can be used as a useful index for determining the timing of maintenance and replacement of the rotating machinery equipment 4.

[0103] In this embodiment, an example has been shown in which the abnormality index calculation unit 223E calculates the accumulated abnormality time, but an external device different from the abnormality diagnosis device 103 may calculate the accumulated abnormality time.

[0104] Embodiment 4 The abnormality diagnostic device 104 according to this embodiment will be described with reference to FIGS. In the third embodiment, a configuration has been described in which an abnormality due to a failure mode accompanied by a minute torque fluctuation is accurately diagnosed, and further, the total operating time during which an abnormality occurs in the rotating machinery equipment 4 is calculated as the accumulated abnormality time, but this embodiment differs from the third embodiment in that the operating conditions of the rotating machinery equipment 4 that cause the abnormality are estimated. The other configurations are the same as those in the third embodiment, and the same reference numerals are used for the same or corresponding parts as those in the third embodiment.

[0105] The abnormality diagnosis device 104 of this embodiment differs from the abnormality diagnosis device 103 of embodiment 3 in that, as shown in FIG. 23, the monitoring and diagnosis unit 2 includes an abnormality index memory unit 214D and an abnormality index calculation unit 224E instead of the abnormality index memory unit 213D and the abnormality index calculation unit 223E, and further includes an operating condition memory unit 21E. The operating condition storage unit 21E stores the moment-to-moment operating conditions of the rotating machinery equipment 4. For example, when applied to a public plant monitoring and control system such as a water treatment plant, the operating conditions include the flow rate of water fed from a water pump, which is the load equipment 6, the water pressure of the water fed from the water pump, and the opening degree of the valves of the input / output piping that feeds water into or discharges water from the water pump.

[0106] Using Fig. 24, the processing flow of the abnormality diagnosis device 104 in this embodiment will be described along with a detailed description of each component included in the abnormality diagnosis device 104. The processing other than steps S5C, S5D, S5E, and S64 is the same as in embodiment 3, and the same reference numerals are used for the same or corresponding components as in embodiment 3. Note that Fig. 24 corresponds to the processing from after the determination of YES in step S4E to step S7 of the processing flow shown in Fig. 22, and only this portion is extracted and shown for convenience of explanation.

[0107] In step S5C, the abnormality index calculation unit 224E uses the accumulated abnormality time to calculate the accumulated abnormality time per unit operating time of the rotating machinery equipment 4 and the accumulated abnormality time per total operating time, which is a third threshold value. Here, the accumulated abnormality time per unit operating time refers to the proportion of time during which an abnormality occurs in the rotating machinery equipment 4 during unit operation, and the accumulated abnormality time per total operating time refers to the proportion of time during which an abnormality occurs in the rotating machinery equipment 4 during the total operating time. The accumulated abnormality time per unit operation time is calculated by dividing the total operation time during which an abnormality occurs in the rotating machinery equipment 4 during the unit operation time by the unit operation time. The cumulative abnormality time per total operating time is calculated by dividing the total operating time during which an abnormality has occurred in the rotating machinery equipment 4 from the start of operation of the rotating machinery equipment 4 to the present by the operating time from the start of operation of the rotating machinery equipment 4 to the present.

[0108] Then, in step S5D, the abnormality index calculation unit 224E compares the accumulated abnormality time per unit operating time with the accumulated abnormality time per total operating time. If the accumulated abnormality time per unit operating time is equal to or greater than the accumulated abnormality time per total operating time, the abnormality index calculation unit 224E acquires the operating conditions of the rotating machinery equipment 4 that were being executed at that time from the operating condition storage unit 21E.

[0109] Then, in step S5E, when an abnormality occurs in the rotating machinery equipment 4, the abnormality index calculation unit 224E estimates the operating conditions that were actually set for the rotating machinery equipment 4 at that time as the cause of the abnormality. That is, the abnormality index calculation unit 224E estimates the operating conditions of the rotating machinery equipment 4 that cause the abnormality as the abnormality occurrence operating conditions, using the operating conditions acquired from the operating condition storage unit 21E. In addition, the abnormality index calculation unit 224E outputs the abnormality occurrence operating conditions to the abnormality index storage unit 214D.

[0110] Then, in step S64, the diagnostic result output unit 3 acquires the abnormality-occurring operating conditions from the abnormality indicator storage unit 214D. The diagnostic result output unit 3 displays the abnormality-occurring operating conditions on the display unit 31A such as a display, in addition to the diagnostic results acquired from the abnormality determination storage unit 21C in the first or second embodiment, and the external output communication unit 31C outputs the abnormality-occurring operating conditions to an external device such as a control panel, a PC, or a cloud server, thereby notifying a monitoring staff member of the abnormality-occurring operating conditions and monitoring the trend of the abnormality-occurring operating conditions.

[0111] In this way, in addition to the effects of the first or second embodiment, the abnormality diagnosis device 104 of this embodiment estimates the operating conditions of the rotating machinery equipment 4 that cause an abnormality, and thereby notifies the monitoring staff of the operating conditions of the rotating machinery equipment 4 that cause an abnormality along with the abnormality diagnosis results, thereby making it possible to monitor trends in the operating conditions that cause an abnormality. In addition, by estimating the operating conditions that cause an abnormality, it can be used as a useful index for determining the operating conditions of the rotating machinery equipment 4.

[0112] In this embodiment, an example has been shown in which the abnormality index calculation unit 224E calculates the abnormality occurrence operating conditions, but an external device different from the abnormality diagnosis device 104 may calculate the abnormality occurrence operating conditions.

[0113] Embodiment 5. The abnormality diagnostic device 105 according to this embodiment will be described with reference to FIGS. In the fourth embodiment, a configuration for estimating the operating conditions of the rotating machinery equipment 4 that cause an abnormality has been described, but the present embodiment differs from the fourth embodiment in that it calculates the degree of deterioration of the rotating machinery equipment 4. The other configurations are the same as those in the fourth embodiment, and the same reference numerals are used for the same or corresponding parts as those in the fourth embodiment.

[0114] The abnormality diagnosis device 105 of this embodiment differs from the abnormality diagnosis device 104 of embodiment 4 in that, as shown in FIG. 25, the monitoring and diagnosing unit 2 includes an abnormality index storage unit 215D and an abnormality index calculation unit 225E instead of the abnormality index storage unit 214D and the abnormality index calculation unit 224E.

[0115] 26, the processing flow of the abnormality diagnosis device 105 in this embodiment will be described together with a detailed description of each component included in the abnormality diagnosis device 105. The processing other than step S5F and step S65 is the same as in embodiment 4, and the same reference numerals are used to denote the same or equivalent parts as in embodiment 4.

[0116] Note that Figure 26 corresponds to the processing from after the determination of YES in step S4E to step S7 in the processing flow shown in Figure 22, and for convenience of explanation, only this part is extracted and shown.

[0117] In step S5F, the abnormality index calculation unit 225E calculates, as the degree of deterioration progression of the rotating machinery equipment 4, the product of the feature calculated in step S41 or step S42 and the cumulative time of abnormality occurrence calculated in step S5B, the product of the feature calculated in step S41 or step S42 and the cumulative time of abnormality per unit time of the rotating machinery equipment 4 calculated in step S5C, or the product of the feature calculated in step S41 or step S42 and the cumulative time of abnormality per total operating time of the rotating machinery equipment 4 calculated in step S5C. Here, the degree of deterioration is an index indicating the degree of deterioration of the rotating machinery equipment 4 caused by an abnormality that has occurred in the rotating machinery equipment 4.

[0118] Then, in step S65, the diagnosis result output unit 3 acquires the deterioration progress degree from the abnormality index storage unit 215D. The diagnosis result output unit 3 displays the deterioration progress degree on the display unit 31A such as a display in addition to the diagnosis result acquired from the abnormality determination storage unit 21C in the first or second embodiment, and the external output communication unit 31C outputs the deterioration progress degree to an external device such as a control device panel, a PC, or a cloud server, thereby notifying the monitoring staff of the deterioration progress degree and monitoring the trend of the deterioration progress degree.

[0119] In this way, in addition to the effects of the first or second embodiment, the abnormality diagnosis device 105 of this embodiment calculates the degree of deterioration, thereby notifying the monitoring staff of the degree of deterioration along with the abnormality diagnosis result, and making it possible to monitor the trend of the degree of deterioration. In addition, by comparing the degrees of deterioration of a plurality of rotating machinery equipment 4, it can be used as a useful index for determining the timing of maintenance and replacement of the rotating machinery equipment 4.

[0120] In this embodiment, an example has been shown in which the abnormality index calculation unit 225E calculates the deterioration progression degree, but an external device different from the abnormality diagnosis device 105 may calculate the deterioration progression degree.

[0121] Embodiment 6 In the first or second embodiment, a configuration for accurately diagnosing abnormalities due to failure modes accompanied by minute torque fluctuations is described; in the third embodiment, a configuration for calculating the total operating time during which an abnormality occurs in the rotating machinery equipment 4 as the accumulated abnormality time is described; in the fourth embodiment, a configuration for estimating the operating conditions of the rotating machinery equipment 4 that cause the abnormality is described; and in the fifth embodiment, a configuration for calculating the degree of deterioration of the rotating machinery equipment 4 is described.

[0122] This embodiment differs from embodiments 1 to 5 in that the operating conditions are controlled to suppress the progression of deterioration based on the abnormality detection or accumulated abnormality time, the operating conditions of the rotating machinery equipment 4 that cause the abnormality, and the degree of deterioration of the rotating machinery equipment 4. For example, as mentioned above, cavitation is a failure mode that accompanies minute torque fluctuations in pump equipment, which is one of the rotating machinery equipment 4. Cavitation occurs when the liquid flow rate increases locally inside the pump, causing a drop in pressure, and when the pressure drops below the saturated vapor pressure of the liquid, the liquid vaporizes. Furthermore, the sudden change in volume when the vapor returns to liquid causes an impact that damages the pump. To suppress this type of cavitation, the rotational speed of the rotating machinery equipment 4 is reduced below the operating conditions under which cavitation occurs, thereby reducing the liquid flow rate.

[0123] Although cavitation suppression has been described as a proposal, in the first or second embodiment, when an abnormality is detected, the occurrence of the abnormality can be suppressed by feedback control of the rotation speed of the rotating machinery equipment 4. Furthermore, in the third embodiment, control for suppressing the occurrence of an abnormality in accordance with the accumulated abnormality time calculated may be fed back to the rotating machinery equipment 4. Furthermore, in the fifth embodiment, control for suppressing the occurrence of an abnormality in accordance with the degree of deterioration progress calculated may be fed back to the rotating machinery equipment 4. Furthermore, in the fourth embodiment, control for operating the rotating machinery equipment 4 in a manner that avoids the operating conditions that cause the abnormality estimated may be fed back. In this case, it is necessary for the operating condition storage unit that stores the operating conditions of the rotating machinery equipment to store the operating conditions of the rotating machinery equipment, which are the previous operating conditions of the rotating machinery equipment that were executed when the abnormality was diagnosed.

[0124] Although the present application describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to application to a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are conceivable within the scope of the technology disclosed in the present specification, including, for example, cases where at least one component is modified, added, or omitted, and cases where at least one component is extracted and combined with components of another embodiment. [Explanation of symbols]

[0125] 1 Current detection unit, 2 Monitoring and diagnosis unit, 3 Diagnosis result output unit, 4 Rotating machinery equipment, 5 Electric motor, 6 Load equipment, 7 Inverter, 8 Commercial power supply, 101, 102, 103, 104, 105 Abnormality diagnosis device.

Claims

1. An abnormality diagnosis device for diagnosing abnormalities in rotating machinery equipment, a current signal storage unit for storing a current signal of the electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result that is a result of the frequency analysis performed by the frequency analysis unit on the waveform of the current signal; a feature amount calculation unit that detects the plurality of spectral peaks from the data belonging to the characteristic frequency band, excludes data detected as the plurality of spectral peaks from the data belonging to the characteristic frequency band, and calculates a sum of signal intensities included in the data belonging to the characteristic frequency band excluding the data detected as the plurality of spectral peaks; an abnormality diagnosis unit that diagnoses the rotating machinery equipment as abnormal when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, When the number of data items in the sum is different from the number of data items of the first threshold, the feature calculation unit excludes data items with higher signal strength from the data items of the first threshold in order, or excludes data items with higher signal strength from the data items of the sum, in order, to match the number of data items in the sum with the number of data items of the first threshold.

2. An abnormality diagnosis device for diagnosing abnormalities in rotating machinery equipment, a current signal storage unit for storing a current signal of the electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result that is a result of the frequency analysis performed by the frequency analysis unit on the waveform of the current signal; a feature amount calculation unit that sorts the data belonging to the characteristic frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the characteristic frequency band, and calculates the sum of the signal strengths included in the data belonging to the characteristic frequency band, excluding the data whose signal strength is equal to or greater than the second threshold; an abnormality diagnosis unit that diagnoses the rotating machinery equipment as abnormal when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, When the number of data items in the sum is different from the number of data items of the first threshold, the feature calculation unit excludes data items with higher signal strength from the data items of the first threshold in order, or excludes data items with higher signal strength from the data items of the sum, in order, to match the number of data items in the sum with the number of data items of the first threshold.

3. 3. The abnormality diagnosis device according to claim 1, wherein the signal strength is a current value or a current power spectrum of a current signal.

4. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from a zeroth-order component to a second-order component of a power supply frequency.

5. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from a fourth-order component to a sixth-order component of a power supply frequency.

6. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from a sixth-order component to an eighth-order component of a power supply frequency.

7. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from 0 Hz to 120 Hz.

8. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from 200 Hz to 360 Hz.

9. 4. The abnormality diagnosis device according to claim 3, wherein the predetermined frequency range is a frequency range from 300 Hz to 480 Hz.

10. a current detection unit connected to a wiring that connects the electric motor to a commercial power source that supplies power to the electric motor, that detects a current that drives the electric motor, and that outputs a current signal of the detected current to the current signal storage unit; The abnormality diagnosis device according to claim 3 , further comprising:

11. an operating condition storage unit that stores operating conditions of the rotary machinery equipment, 3. The abnormality diagnosis device according to claim 1, wherein the rotating machinery equipment is operated under an operating condition that would have been executed if an abnormality had been diagnosed.

12. The abnormality diagnosing device according to claim 3 , further comprising: an abnormality determination storage unit that stores a diagnosis result of the abnormality diagnosing unit when the abnormality diagnosing unit diagnoses the rotating machinery equipment as abnormal.

13. a diagnostic result output unit including at least one of a display unit that displays the diagnostic result, an alarm unit that issues an alarm when the rotating machinery equipment is diagnosed as abnormal, and an external output communication unit that transmits the diagnostic result to an external device; The abnormality diagnosis device according to claim 12, further comprising:

14. an abnormality index calculation unit that calculates an abnormality cumulative time, which is the total operating time during which an abnormality occurs in the rotating machinery equipment, using the diagnosis results stored in the abnormality determination storage unit; The abnormality diagnosis device according to claim 12, further comprising:

15. an operating condition storage unit that stores operating conditions of the rotary machinery equipment, 15. The abnormality diagnosis device according to claim 14, wherein the abnormality index calculation unit calculates the accumulated abnormality time per unit operating time of the rotating machinery equipment, and when the accumulated abnormality time per unit operating time of the rotating machinery equipment becomes equal to or greater than a third threshold, estimates the operating conditions of the rotating machinery equipment that were being executed at that time as abnormality-occurring operating conditions that cause an abnormality in the rotating machinery equipment.

16. 16. The abnormality diagnosis device according to claim 15, wherein the rotating machinery equipment is operated while avoiding the operating conditions under which the abnormality occurs.

17. 15. The abnormality diagnosis device according to claim 14, wherein the abnormality index calculation unit calculates the accumulated abnormality time per total operating time of the rotating machinery equipment, and calculates a deterioration progression degree which is the product of the sum and the accumulated abnormality time, the product of the sum and the accumulated abnormality time per unit operating time of the rotating machinery equipment, or the product of the sum and the accumulated abnormality time per total operating time of the rotating machinery equipment.

18. an operating condition storage unit that stores operating conditions of the rotary machinery equipment, 18. The abnormality diagnosis device according to claim 17, wherein the rotating machinery equipment is operated by avoiding the operating conditions that would have been executed if an abnormality had been diagnosed, depending on the degree of deterioration.

19. An abnormality diagnosis system for diagnosing abnormalities in rotating machinery equipment, comprising: a current signal storage unit for storing a current signal of the electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result that is a result of the frequency analysis performed by the frequency analysis unit on the waveform of the current signal; a feature amount calculation unit that detects the plurality of spectral peaks from the data belonging to the characteristic frequency band, excludes data detected as the plurality of spectral peaks from the data belonging to the characteristic frequency band, and calculates a sum of signal intensities included in the data belonging to the characteristic frequency band excluding the data detected as the plurality of spectral peaks; an abnormality diagnosis unit that diagnoses the rotating machinery equipment as abnormal when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, When the number of data items in the sum is different from the number of data items of the first threshold, the feature calculation unit excludes data items with higher signal strength from the data items of the first threshold in order, or excludes data items with higher signal strength from the data items of the sum in order, thereby matching the number of data items in the sum with the number of data items of the first threshold.

20. An abnormality diagnosis system for diagnosing abnormalities in rotating machinery equipment, comprising: a current signal storage unit for storing a current signal of the electric motor; a frequency analysis unit that performs frequency analysis on the waveform of the current signal stored in the current signal storage unit; a characteristic frequency band extraction unit that extracts data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result that is a result of the frequency analysis performed by the frequency analysis unit on the waveform of the current signal; a feature amount calculation unit that sorts the data belonging to the characteristic frequency band in order of signal strength, excludes data whose signal strength is equal to or greater than a second threshold from the data belonging to the characteristic frequency band, and calculates the sum of the signal strengths included in the data belonging to the characteristic frequency band, excluding the data whose signal strength is equal to or greater than the second threshold; an abnormality diagnosis unit that diagnoses the rotating machinery equipment as abnormal when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, When the number of data items in the sum is different from the number of data items of the first threshold, the feature calculation unit excludes data items with higher signal strength from the data items of the first threshold in order, or excludes data items with higher signal strength from the data items of the sum in order, thereby matching the number of data items in the sum with the number of data items of the first threshold.

21. an operating condition storage unit that stores operating conditions of the rotary machinery equipment, 21. The abnormality diagnosis system according to claim 19, wherein the rotating machinery equipment is operated by avoiding the operating conditions of the rotating machinery equipment that would have been executed if an abnormality had been diagnosed.

22. An abnormality diagnosis method for diagnosing an abnormality in a rotating machinery facility, comprising: a current signal detection step of detecting a current signal flowing through the electric motor; a frequency analysis step of frequency-analyzing the waveform of the current signal detected in the current signal detection step; a characteristic frequency band extraction step of extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of detecting the plurality of spectral peaks from the data belonging to the characteristic frequency band and excluding data detected as the plurality of spectral peaks from the data belonging to the characteristic frequency band; a feature calculation step of calculating a sum of signal intensities included in the data belonging to the feature frequency band excluding the data detected as the plurality of spectral peaks; a determining step of determining whether the sum is equal to or greater than a first threshold; an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, The abnormality diagnosis method is characterized in that, when the number of data in the sum is different from the number of data in the first threshold, the feature calculation step excludes data with increasing signal strength from the data in the first threshold in order, or excludes data with decreasing signal strength from the data in the sum in order, so as to match the number of data in the sum with the number of data in the first threshold.

23. An abnormality diagnosis method for diagnosing an abnormality in a rotating machinery facility, comprising: a current signal detection step of detecting a current signal flowing through the electric motor; a frequency analysis step of frequency-analyzing the waveform of the current signal detected in the current signal detection step; a characteristic frequency band extraction step of extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of sorting the data belonging to the characteristic frequency band in order of signal strength and excluding data whose signal strength is equal to or greater than a second threshold from the data belonging to the characteristic frequency band; a feature calculation step of calculating a sum of the signal strengths included in the data belonging to the feature frequency band excluding data whose signal strength is equal to or greater than the second threshold; a determining step of determining whether the sum is equal to or greater than a first threshold; an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold value; Equipped with the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, The abnormality diagnosis method is characterized in that, when the number of data in the sum is different from the number of data in the first threshold, the feature calculation step excludes data with increasing signal strength from the data in the first threshold in order, or excludes data with decreasing signal strength from the data in the sum in order, so as to match the number of data in the sum with the number of data in the first threshold.

24. an operating condition storage step of storing operating conditions of the rotary machinery equipment, The abnormality diagnosis method according to claim 22 or 23, characterized in that the rotating machinery equipment is operated by avoiding the operating conditions of the rotating machinery equipment that were being executed when the rotating machinery equipment was diagnosed as abnormal.

25. A program for diagnosing abnormalities in rotating machinery equipment, On the computer, a current signal detection step of detecting a current signal flowing through the electric motor; a frequency analysis step of frequency-analyzing the waveform of the current signal detected in the current signal detection step; a characteristic frequency band extraction step of extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of detecting the plurality of spectral peaks from the data belonging to the characteristic frequency band and excluding data detected as the plurality of spectral peaks from the data belonging to the characteristic frequency band; a feature calculation step of calculating a sum of signal intensities included in the data belonging to the feature frequency band excluding the data detected as the plurality of spectral peaks; a determining step of determining whether the sum is equal to or greater than a first threshold; an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold value; Execute the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, The feature calculation step, when the number of data in the sum is different from the number of data in the first threshold, removes data with higher signal strength from the data in the first threshold in order, or removes data with higher signal strength from the data in the sum in order, thereby matching the number of data in the sum with the number of data in the first threshold.

26. A program for diagnosing abnormalities in rotating machinery equipment, On the computer, a current signal detection step of detecting a current signal flowing through the electric motor; a frequency analysis step of frequency-analyzing the waveform of the current signal detected in the current signal detection step; a characteristic frequency band extraction step of extracting data belonging to a characteristic frequency band so as to include a plurality of spectral peaks within a predetermined frequency range from a frequency analysis result obtained by frequency analyzing the waveform of the current signal in the frequency analysis step; a data excluding step of sorting the data belonging to the characteristic frequency band in order of signal strength and excluding data whose signal strength is equal to or greater than a second threshold from the data belonging to the characteristic frequency band; a feature calculation step of calculating a sum of the signal strengths included in the data belonging to the feature frequency band excluding data whose signal strength is equal to or greater than the second threshold; a determining step of determining whether the sum is equal to or greater than a first threshold; an abnormality diagnosis step of diagnosing an abnormality in the rotating machinery equipment when the sum is equal to or greater than a first threshold value; Execute the first threshold value is a value obtained by performing statistical processing on the sum accumulated for a predetermined period from the start of operation of the rotating machinery equipment, The feature calculation step, when the number of data in the sum is different from the number of data in the first threshold, removes data with higher signal strength from the data in the first threshold in order, or removes data with higher signal strength from the data in the sum in order, thereby matching the number of data in the sum with the number of data in the first threshold.

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