Abnormality diagnosis device and abnormality diagnosis method

Abstract

An abnormality diagnosis device (100) that performs abnormality determination for a load device (10) including an electric motor (5) and load equipment (7) connected to the electric motor (5) is configured so as to perform FFT analysis of a current of the electric motor (5), calculate the absolute values of the differences between the signal strengths at the respective frequencies of a plurality of spectrum peaks extracted during the diagnosis and signal strengths during a normal time, and performs the abnormality determination treating the sum of said absolute values as a feature quantity.

Description

Abnormality diagnosis device and abnormality diagnosis method

[0001] The present application relates to an abnormality diagnosis device and an abnormality diagnosis method.

[0002] In a plant, there are many load facilities connected to electric motors via power transmission mechanisms, and abnormality diagnosis is performed on the electric motors, power transmission mechanisms, and load facilities for maintenance purposes.

[0003] In response to this problem, it is known that for an electric motor, which is the driving source of a rotating mechanical system, sidebands related to abnormalities in the rotating mechanical system are extracted from a spectral pattern obtained by performing a fast Fourier transform on the current signal of the electric motor while it is operating, and abnormalities are detected from the intensity of the sidebands (see, for example, Patent Document 1).

[0004] JP 2017-181437 A

[0005] However, in the technology disclosed in Patent Document 1, the relationship between the strength of the sideband waves and whether or not there is an abnormality changes depending on the load torque, so there is a problem in that the diagnostic accuracy decreases depending on the selection of the frequency and load torque to be focused on.

[0006] The present application discloses technology for solving the above-mentioned problems, and aims to provide an abnormality diagnosis device and an abnormality diagnosis method that are capable of accurately diagnosing abnormalities in load devices that include electric motors, such as rotating mechanical systems.

[0007] An abnormality diagnosis device according to the present disclosure is an abnormality diagnosis device for diagnosing an abnormality in a load device including an electric motor and load equipment connected to the electric motor, the abnormality diagnosis device comprising: a current detector for detecting a current in the electric motor; and an abnormality diagnosis unit for diagnosing an abnormality using spectral peaks extracted by FFT analysis of the current detected by the current detector, wherein the abnormality diagnosis unit comprises: a peak analysis unit for performing analysis using a power supply frequency of the electric motor and frequencies of sidebands relative to the power supply frequency, and analyzing the extracted spectral peaks by frequency; a frequency determination unit for determining, from the spectral peaks analyzed by the peak analysis unit, which type of spectral peak is attributable to the electric motor or the load equipment; a frequency storage unit for storing, when the load equipment is normal, the frequencies and signal strengths of spectral peaks attributable to the electric motor and the frequencies and signal strengths of spectral peaks attributable to the load equipment for each load torque of the electric motor; and an abnormality determination unit, wherein the abnormality determination unit comprises: a load torque selection unit for selecting a load torque when a current is detected by the current detector during abnormality diagnosis; The device is equipped with a feature quantity calculation unit that performs FFT analysis on the current detected during abnormality diagnosis, reads out from the frequency storage unit the frequency and signal strength of the spectrum peak in normal state corresponding to the load torque selected by the load torque selection unit and the type determined for a plurality of diagnostic spectrum peaks, calculates the absolute value of the difference in signal strength from the normal state for each frequency for the plurality of diagnostic spectrum peaks, and uses the sum of the calculated absolute values ​​as a feature quantity; and a feature quantity determination unit that compares the feature quantity with a preset threshold value to determine an abnormality in the load device.

[0008] According to the abnormality diagnosis device and abnormality diagnosis method of the present disclosure, it is possible to accurately diagnose abnormalities in a load device including an electric motor.

[0009] 10A is a diagram for explaining a schematic configuration of an abnormality diagnosis device according to embodiment 1. FIG. 10B is a diagram showing an example of the configuration of a control device that drives an electric motor according to embodiment 1. FIG. 10C is a block diagram showing the configuration of an abnormality diagnosis unit according to embodiment 1. FIG. 10D is a diagram showing an example of a spectral waveform that is an example of an analysis result by a current FFT when belt slack is progressing under no load. FIG. 10E is a diagram showing an example of a spectral waveform that is an example of an analysis result by a current FFT when belt slack is progressing under rated load. FIG. 10F is a flowchart showing an overall procedure for performing abnormality diagnosis using the abnormality diagnosis device according to embodiment 1. FIG. 10F is a flowchart showing a procedure for calculating feature quantities. FIG. 10A is a diagram showing a relationship between belt tension and feature quantities under no load. FIG. 10B is a diagram showing the relationship between belt tension and signal strength of the belt rotation frequency (-1st order) as a comparative example. 11A is a diagram illustrating the relationship between belt tension and feature quantity at rated load, and FIG. 11B is a diagram illustrating the relationship between belt tension and signal strength of belt rotation frequency (-1st order) as a comparative example. FIG. 11B is a diagram illustrating the schematic configuration of an abnormality diagnosis device according to embodiment 2. FIG. 11C is a diagram illustrating an example of function fitting between belt tension and feature quantity. FIG. 11D is a diagram illustrating an example of the hardware configuration of an abnormality diagnosis device according to embodiments 1 and 2. FIG. 11E is a diagram illustrating another example of the hardware configuration of an abnormality diagnosis device according to embodiments 1 and 2.

[0010] The present embodiment will be described below with reference to the drawings. In the drawings, the same reference numerals indicate the same or corresponding parts.

[0011] Embodiment 1. An abnormality diagnosis device for a rotating machine system according to embodiment 1 will now be described with reference to the drawings. FIG. 1 is a diagram illustrating the general configuration of the abnormality diagnosis device according to embodiment 1. The abnormality diagnosis device 100 detects an abnormality in a load device 10 and performs an abnormality diagnosis. The load device 10 includes an electric motor 5, a power transmission mechanism 6 that transmits power from the electric motor 5 to load equipment 7, and the load equipment 7, and the abnormality diagnosis device 100 detects an abnormality in the electric motor 5, an abnormality in the power transmission mechanism 6, and an abnormality in the load equipment 7 and performs an abnormality diagnosis. In the drawing, the electric motor 5 is an example of one of many motors used in, for example, a plant, and is connected to a power supply 2 for driving the electric motor via a power line via an electric motor control device 110.

[0012] <Configuration of Abnormality Diagnosis Device 100> The abnormality diagnosis device 100 includes a motor control device 110, an abnormality diagnosis unit 130, an output unit 140, and a current detector 120 connected to one of the three-phase power lines connected to the motor 5. The abnormality diagnosis device 100 may be mounted in a motor control center that manages a large number of electric motors installed in a plant or the like, or may be a motor diagnosis device provided separately from the motor control center. The motor control device 110 may be provided in the abnormality diagnosis device 100, or may be provided independently of the abnormality diagnosis device 100.

[0013] The current detector 120 may be, for example, a clamp-type current transformer (CT) and installed on each phase of the three-phase power line. However, it is sufficient to measure only one of the phases. Furthermore, the installation location of the current detector 120 is not limited as long as it can measure the drive current of the electric motor 5. This indicates that the detection accuracy does not change depending on the measurement location. In this embodiment, an example of detecting a u-phase current will be described. Furthermore, if current waveform acquisition and abnormality diagnosis are performed continuously, memory pressure or an overload of the arithmetic processing circuit due to abnormality diagnosis may occur. If the target equipment is not expected to experience sudden abnormalities, data should not be acquired continuously, but should be acquired appropriately at the timing of diagnosis. During steady-state operation, current data should be acquired once per hour.

[0014] The output unit 140 displays the diagnosis results from the abnormality diagnosis unit 130, or outputs an audible or visual alarm based on the diagnosis results to notify of abnormalities in the motor and the power transmission mechanism. Furthermore, when the entire plant is monitored by a monitoring and control device or the like and abnormal conditions are summarized, they are transmitted using a communication function.

[0015] 2 is a diagram for explaining the configuration of the motor control device 110. The motor control device 110 is provided with an inverter (power conversion device) 111 and a control unit 112 for driving the inverter 111. For example, if the inverter 111 is configured with semiconductor switching elements, the semiconductor switching elements of the inverter 111 are driven and controlled so as to perform power conversion using a PWM (Pulse Width Modulation) method or the like from a carrier wave and a rectangular wave generated by the control unit 112. Here, the frequency of the carrier wave that is the basis for driving the inverter 111 is the power supply frequency f of the motor. ｓ The electric power converted by the inverter 111 is supplied to the electric motor 5. That is, the electric motor 5 is driven and controlled by the inverter 111.

[0016] The power transmission mechanism 6 is composed of, for example, an electric motor side pulley Pu1 connected to the rotating shaft of the electric motor 5 and a load equipment side pulley Pu2 connected to the drive shaft of the load equipment 7, with a power transmission member, for example, a belt 61, wrapped around them.

[0017] As described above, the load device 10 may be composed of the electric motor 5, the power transmission mechanism 6, and the load equipment 7, or may be composed of the electric motor 5 and the load equipment 7 without a power transmission mechanism. In this embodiment, the electric motor 5, the power transmission mechanism 6, and the load equipment 7 are not particularly limited in type. For example, the electric motor may be a single-phase or three-phase induction machine or a synchronous machine, and the power transmission mechanism may be a belt, gear, coupling, or the like. Examples of the load equipment include a pump and a fan. Furthermore, if no power transmission mechanism is provided, the electric motor and the load equipment may be directly connected.

[0018] <Configuration of Abnormality Diagnosis Unit 130> Next, a description will be given of the configuration of the abnormality diagnosis unit 130. Fig. 3 is a block diagram showing the configuration of the abnormality diagnosis unit 130 of the abnormality diagnosis device 100 according to embodiment 1. In Fig. 3, the abnormality diagnosis unit 130 includes a load device setting unit 131, a first storage unit 132, a second storage unit 133, a calculation unit 135, and a diagnosis result storage unit 136.

[0019] The load device setting unit 131 is used to set information about the electric motor 5, information about the power transmission mechanism 6, and information about the load equipment 7. The load device setting unit 131 is used to acquire specifications of the electric motor 5, such as the power supply frequency, number of poles, and rated rotation speed, from information on a nameplate attached to the electric motor 5. Using this information, the rotation frequency of the electric motor 5 is identified online in real time with high accuracy, and is used to detect mechanical abnormalities in the electric motor.

[0020] If the power transmission mechanism 6 is a belt, the load device setting unit 131 performs settings to recognize that a belt is attached. If the power transmission mechanism 6 does not exist, the load device setting unit 131 performs settings to recognize that there is no power transmission mechanism 6. The load device setting unit 131 performs settings corresponding to the type of load equipment 7. For example, if it is a fan, the load device setting unit 131 sets conditions such as the fan rotation speed.

[0021] The specification information of the electric motor 5, the information of the power transmission mechanism, and the information of the load equipment 7 are stored in the first storage unit 132. The first storage unit 132 also stores the drive current of the electric motor 5 acquired by the current detector 120. In this embodiment, the value of the load torque at the time of current detection is also stored together with the value of the detected u-phase current.

[0022] The second storage unit 133 includes a determination criterion storage unit 133 a, an electric motor frequency storage unit 133 b, a power transmission mechanism frequency storage unit 133 c, and a load equipment frequency storage unit 133 d. The determination criterion storage unit 133 a is used to store thresholds for determining abnormalities in the electric motor 5, the power transmission mechanism 6, and the load equipment 7.

[0023] The motor frequency storage unit 133b stores the frequency value of the spectrum peak caused by the motor 5. In addition to the frequency value, the signal strength of the spectrum peak, the power supply frequency fｓ It is preferable to store the signal strength of the spectrum peak of the power supply frequency and the load torque of the electric motor 5 in the same manner. In either case, the results of analysis by the calculation unit 135, which will be described later, are stored. Alternatively, if the generated frequency is known in advance, it may be acquired and set by the load device setting unit 131. The load torque may be measured simultaneously when acquiring the current data of the electric motor 5, or may be calculated by a known method using the acquired current waveform of the electric motor 5.

[0024] The power transmission mechanism frequency storage unit 133c stores the frequency value of the spectrum peak caused by the power transmission mechanism 6. In addition to the frequency value, the signal intensity of the spectrum peak caused by the power transmission mechanism 6, the power supply frequency f when the spectrum peak caused by the power transmission mechanism 6 was acquired, and the like are also stored. ｓ , the signal strength of the power supply frequency spectrum peak, and the load torque of the electric motor 5 are preferably stored as well.

[0025] The load equipment frequency storage unit 133d stores the frequency value of the spectrum peak caused by the load equipment 7. In addition to the frequency value, the signal strength of the spectrum peak caused by the load equipment 7, the power frequency f ｓ , the signal strength of the power supply frequency spectrum peak, and the load torque of the electric motor 5 are preferably stored as well.

[0026] The calculation unit 135 includes a spectrum analysis unit 135 a, a sideband analysis unit 135 b, a peak analysis unit 135 c, a frequency determination unit 135 d, and an abnormality determination unit 135 f. The spectrum analysis unit 135 a uses the current detected by the current detector 120 to perform a current FFT (Fast Fourier Transform) analysis (frequency analysis).

[0027] The sideband wave analysis unit 135b detects all spectral peaks from the spectral waveform analyzed by the spectrum analysis unit 135a. The frequency range to be detected is preferably between 0 and 1000 Hz. Next, from among the detected spectral peaks, it determines which spectral peaks satisfy the sideband wave conditions.

[0028] The peak analysis unit 135c analyzes the sidebands extracted by the sideband analysis unit 135b by frequency. Based on the results of the analysis by the peak analysis unit 135c, the frequency determination unit 135d determines whether the sidebands are spectral peaks caused by the electric motor, the power transmission mechanism, or the load equipment. When a frequency is determined to be of a certain type, the frequency value of the spectral peak is stored in each of the frequency storage units 133b, 133c, and 133d of the second storage unit 133. At the same time, the signal strength of the spectral peak, the operating frequency of the motor control device 110 when the spectral peak was acquired, the signal strength of the spectral peak at the operating frequency, and the load torque of the electric motor 5 are preferably also stored.

[0029] The abnormality determination unit 135f includes a load torque selection unit 135f1, a feature amount calculation unit 135f2, and a feature amount determination unit 135f3, and determines whether or not there is an abnormality in the electric motor 5, the power transmission mechanism 6, or the load equipment 7. The load torque selection unit 135f1 selects the load torque of the electric motor 5 during operation or the load torque for which abnormality determination is performed. The feature amount calculation unit 135f2 calculates a feature amount based on the frequency of the spectrum peak corresponding to the selected load torque. The feature amount determination unit 135f3 compares the calculated feature amount with a threshold value stored in advance in the determination criterion storage unit 133a to determine whether or not there is an abnormality.

[0030] The diagnosis result storage unit 136 stores the result of the determination made by the abnormality determination unit 135 f and outputs it to the output unit 140 .

[0031] The abnormality diagnosis device 100 according to the first embodiment will be described in detail using an example configuration in which the load equipment 7 is a fan, the power transmission mechanism 6 is the belt 61 shown in FIG. 2 , and the electric motor 5 is a three-phase induction machine. An abnormality in the tension of the belt in the power transmission mechanism 6 will be described as an example of an abnormality to be detected. In a load device 10 using a belt in the power transmission mechanism 6, the tension of the belt gradually decreases when the load device 10 is driven for a long period of time, and the belt must be replaced at an appropriate time. This example was selected because there is a growing demand for automating the timing of replacement. Therefore, spectrum peak analysis, abnormality determination, and the like will also be described using an abnormality in the tension of the belt in the power transmission mechanism 6 as an example.

[0032] <Analysis of Spectral Peaks in Calculation Unit 135> Next, a description will be given of a method of analyzing spectral peaks in the calculation unit 135. As described above, the spectrum analysis unit 135a performs a current FFT analysis using the current of the electric motor 5 detected by the current detector 120. The measurement time and sampling frequency are set so that the data of the current waveform used in the current FFT analysis can be acquired with sufficient resolution up to the high frequency range.

[0033] The measurement time is related to the resolution, and since the resolution is sufficient to separate the peak of interest from adjacent peaks, it is desirable for it to be around 1 second or more. On the other hand, the sampling frequency is related to the maximum frequency. Since the feature quantity described below is the sum of the signal strength of higher-order components, there are cases where a high frequency range is required depending on the order used. To ensure this, a sampling frequency at least twice the maximum frequency used in the calculation is required.

[0034] The spectral waveform analyzed by the spectrum analysis unit 135a includes spectral peaks attributable to the motor 5, belt 61, and fan that constitute the load device 10. Note that this spectral waveform also includes noise attributable to the motor 5, belt 61, and fan that constitute the load device 10, and therefore it is desirable to average multiple current FFT analyses. As an example, it is sufficient to average the results of about five analyses.

[0035] Next, the spectral waveform analyzed by the spectrum analysis unit 135a will be described. ｍ Specifically, first, let us consider the provisional rotation frequency f when the slip s is set to 0. ｍ０ Calculate the rotation frequency f ｍ０ is the power frequency f ｓ , is expressed as the following equation (1) using the number of poles p. ｍ０ = f ｓ / p (1) where the actual rotation frequency f ｍ is expressed as the following equation (2) using slip s: ｍ = f ｓ(1-s) / p ... (2) From equations (1) and (2), the slip s is s = 1-f ｍ / f ｍ０ ... (3)

[0036] Here, the actual rotation frequency f ｍ is the tentative rotation frequency f ｍ０ near the power supply frequency f ｓ Therefore, the actual rotation frequency f ｍ This actual rotation frequency f ｍ and the tentative rotation frequency f calculated using equation (1) ｍ０ However, this method requires sufficient frequency resolution to calculate the slip, so the data acquisition time must be set accordingly long.

[0037] The sideband analysis unit 135b detects all spectral peaks from the spectral waveform analyzed by the spectrum analysis unit 135a. ｓ 4 shows an example of a spectral waveform obtained by current FFT of the u-phase current of the electric motor 5 connected to a belt, which is a power transmission mechanism 6, when the electric motor 5 is at no-load torque. ｓ A large peak is observed at , and multiple sidebands are observed around the peak. The frequency of the sidebands is the power supply frequency f ｓ , each rotation frequency is f ｉ When the order of the harmonic is a natural number n, it is expressed as the following equation (4): f = f ｓ ±nf ｉ ...(4)

[0038] As described above, the spectrum includes the spectrum peaks caused by the electric motor 5, the belt 61, and the fan which is the load equipment 7. The rotation frequency f ｍ is expressed by the above-mentioned formula (2), and the rotation frequency f ｌ and the rotation frequency f due to the belt 61 of the power transmission mechanism 6ｂ are expressed as the following equations (5) and (6), respectively. ｌ = f ｓ (1-s) / pr...(5) f ｂ = 2πr ｍ f ｍ / l ｂ = 2πr ｌ f ｌ / l ｂ ...(6) where r: reduction ratio r ｍ : Diameter of motor side pulley Pu1 r ｌ : Diameter of the load equipment side pulley Pu2 l ｂ : Belt length.

[0039] In this way, the rotation frequency f ｍ , the rotation frequency caused by the fan f ｌ and the rotation frequency f due to the belt 61 ｂ can be calculated from the shapes and drive information of the electric motor 5, the fan, and the belt 61. The peak analysis unit 135c analyzes the sidebands extracted by the sideband analysis unit 135b according to the frequencies caused by each sideband.

[0040] Although the details will be described later, in FIG. ｓ The rotation frequency f caused by the belt 61 is ｂ The first to third sidebands are shown.

[0041] The frequency determination unit 135d determines the rotation frequency f ｍ , the rotation frequency caused by the fan f ｌ and the rotation frequency f due to the belt 61 ｂ For each type, the frequencies from first to higher orders and their intensities in a normal state are stored in the second storage unit 133 together with the load torque value obtained from the current used in the current analysis. For example, according to the analysis results of FIG. 4, from the spectrum in a normal state shown by the solid line, the spectrum peak caused by the belt 61 is f = f ｓ ±nf ｂThe frequencies (n is 1 to 3), the signal strength at each frequency, and the load torque 0 are acquired and stored in the power transmission mechanism frequency storage unit 133c.

[0042] 5 shows an example of a spectral waveform obtained by current FFT of the u-phase current of the electric motor 5 connected to the belt, which is the power transmission mechanism 6, at the rated load of the electric motor 5. From the spectrum in a normal state shown by the solid line in the analysis result of FIG. 5, the spectral peak caused by the belt 61 is f = f ｓ ±nf ｂ The frequencies (n is 1 to 3), the signal strength at each frequency, and the load torque value (at rated load) are acquired and stored in the power transmission mechanism frequency storage unit 133c. In this way, the frequency storage units 133b, 133c, and 133d store the frequencies, signal strengths, and load torque values ​​of the respective spectrum peaks under normal conditions.

[0043] During abnormality diagnosis, a load torque selection unit 135f1 of the abnormality determination unit 135f reads, from the load torque detected and subjected to FFT analysis at the time of current acquisition, data such as the frequency of the spectrum peak corresponding to the corresponding load torque and the signal intensity of each frequency from the normal data stored in the frequency storage units 133b, 133c, and 133d of the second storage unit 133. In this example, an abnormality in the belt 61 is used as the target of abnormality diagnosis, so data is read from the power transmission mechanism frequency storage unit 133c.

[0044] The load torque may be measured when detecting the current data and stored according to the measurement results, but it is also possible to select the load torque in the load torque selection unit using the current waveform. Using the current waveform eliminates the need to measure the load torque, eliminating the need for equipment for measuring the load torque, thereby reducing costs. It also has the advantage of eliminating the need to store the load torque state in memory, thereby reducing memory capacity. As a method for selecting the load torque using the current waveform, since the current amplitude increases as the load torque increases, the load torque is selected for each current amplitude. Any method other than the current amplitude that can determine the magnitude of the current value, such as the effective value of the current, may be used to select the load torque. Another method is to focus on the change in the rotational frequency accompanying the change in the slip s described above. The slip s increases as the load torque increases. From equation (2), the rotational frequency f ｍ Since s is a variable, when the load torque increases, the rotation frequency f ｍ The position where the peak occurs also changes.

[0045] The feature value calculation unit 135f2 compares the frequency and signal strength of the diagnostic spectrum peak analyzed by the peak analysis unit 135c and determined by the frequency determination unit 135d with the frequency and signal strength of the normal spectrum peak corresponding to the selected load torque read from the power transmission mechanism frequency storage unit 133c, and calculates the difference in signal strength at each frequency. For example, in FIG. 4, if the solid line represents the normal spectrum waveform and the dashed line (abnormal) represents the spectrum waveform at the time of abnormality diagnosis, the feature value calculation unit 135f2 calculates the difference in signal strength at each frequency from the first order to the frequency of the higher-order spectrum peak (up to the third order in FIG. 4), and calculates the sum of the absolute values ​​of the differences as the feature value C. ｂ If a normal solid line spectrum waveform is obtained even during abnormality diagnosis, the feature quantity C ｂ is 0.

[0046] The characteristic amount determination unit 135f3 determines the characteristic amount C at the time of the belt abnormality diagnosis calculated by the characteristic amount calculation unit 135f2. ｂ is the threshold value B for belt abnormality diagnosis stored in the judgment criterion storage unit 133a. ｔｈIf it does, it is determined that there is an abnormality.

[0047] The result of the determination by the feature amount determination unit 135 f 3 is stored in the diagnosis result storage unit 136 and is also output to the output unit 140 .

[0048] <Procedure for Abnormality Diagnosis Using Abnormality Diagnosis Device 100> Next, the operation of the abnormality diagnosis device 100 will be described with reference to Fig. 6 and Fig. 7. Fig. 6 is a flowchart showing the overall process for performing abnormality diagnosis using the abnormality diagnosis device 100 according to the first embodiment, and Fig. 7 is a flowchart showing the procedure for calculating feature quantities.

[0049] First, the overall process of abnormality diagnosis will be described. In step S101, data for the object to be diagnosed when normal is acquired for each load torque. Specifically, data for when the tension of the belt 61 is normal is acquired by detecting the u-phase current of the electric motor 5 using the current detector 120, and the load torque is also acquired by measurement or calculation. The detected current is subjected to current FFT analysis by the spectrum analysis unit 135a as described above, and spectral peaks are detected from the analyzed spectral waveform by the sideband analysis unit 135b. The detected sidebands are analyzed by frequency in the peak analysis unit 135c, and the spectral peaks attributable to the power train mechanism determined by the frequency determination unit 135d are stored in the power train mechanism frequency storage unit 133c along with the frequency value of the spectral peak, its signal strength, and the load torque. Note that this process is similar to steps S1021 to S1025 in FIG. 7.

[0050] One method of obtaining normal data is to obtain the current immediately after changing the belt tension, and analyze the data based on that to determine that it is normal. Another method is to store normal data previously measured under the same conditions. Note that, since abnormality diagnosis is performed for each load torque, this normal data must also be stored for each load torque. If normal data has already been obtained, step S101 may be omitted.

[0051] Step S102 and subsequent steps are steps for performing an abnormality diagnosis. In step S102, similar to step S101, the current of the u-phase of the electric motor 5 is detected by the current detector 120, and a current FFT analysis is performed to calculate a feature quantity. In step S103, it is determined whether the calculated feature quantity exceeds a preset threshold value, i.e., whether an abnormality exists. If an abnormality is determined in step S103, a diagnosis result is output in step S104. If no abnormality is determined in step S103, an abnormality diagnosis may be repeatedly performed under different load torque conditions, etc.

[0052] Next, the details of the process of step S102 will be described with reference to Fig. 7. First, in step S1021, the current of the u-phase of the electric motor 5 is detected by the current detector 120 for abnormality diagnosis, and the load torque is also obtained by measurement or calculation. During periodic diagnosis, a load torque may be selected in advance, and the current when the electric motor 5 is operated at a predetermined load torque may be detected.

[0053] In step S1022, the detected current is subjected to current FFT analysis by the spectrum analysis unit 135a as described above. In step S1023, the sideband analysis unit 135b detects spectral peaks from the analyzed spectral waveform. In step S1024, the detected sidebands are analyzed by frequency by the peak analysis unit 135c.

[0054] In step S1025, the frequency determination unit 135d calculates the frequency values ​​and signal intensities of the spectral peaks caused by the power transmission mechanism. At this time, the signal intensities are calculated for the spectral peaks up to the nth order, which are set in advance. The spectral peaks calculated here are those for diagnosis.

[0055] In step S1026, the load torque selection unit 135f1 selects the load torque at the time of current detection in step S1021, and the frequency values ​​and signal intensities of the normal spectrum peaks corresponding to the selected load torque are read from the powertrain mechanism frequency storage unit 133c. At this time, the frequency values ​​and signal intensities of the diagnostic spectrum peaks up to the nth order, whose signal intensities were calculated in step S1025, are read.

[0056] In step S1027, the feature calculation unit 135f2 calculates the difference between the signal intensity of each diagnostic spectral peak that is the target of abnormality diagnosis calculated in step S1025 and the signal intensity of the normal spectral peak that was read out in step S1026. Next, in step S1028, the absolute values ​​of the calculated differences in signal intensity between the diagnostic and normal states are added up to the nth order, and the sum is defined as the feature C ｂ It is calculated as follows.

[0057] <Method of Abnormality Determination> Next, the method of abnormality determination according to this embodiment and its effects will be described. The spectral waveforms in abnormal states shown by the dashed lines in Figures 4 and 5 above are both examples of a state in which the belt is becoming loose. The spectral waveforms in abnormal states shown by the dashed lines in Figures 8 and 9 are both examples of a state in which the belt is starting to loosen.

[0058] In the state where the belt loosens as shown in FIGS. 4 and 5, the rotation frequency f due to the belt 61 decreases from the normal state when there is no load (load torque 0) as shown in FIG. ｂ The signal strength of the second-order component of the signal f decreases, but the third-order component increases. Also, as shown in Figure 5, at rated load (load torque is rated), the second-order component hardly changes, but only the third-order component increases. From the above, it can be seen that when the tension loosens, the rotation frequency f ｂ The third-order component of this is considered to be an effective parameter for abnormality diagnosis.

[0059] 8 and 9, the rotation frequency f due to the belt 61 is lower than that of the normal state when there is no load (load torque 0) in FIG. ｂThe signal intensity of the second-order component of the belt 61 is reduced, but no change is observed in the third-order component. ｂ The second-order component of this is thought to be an effective parameter for diagnosing abnormalities. However, as shown in Figure 9, at rated load (load torque is rated), no significant changes are observed in any of the first, second, or third-order components.

[0060] As described above, it can be seen that the index effective for abnormality diagnosis varies depending on the tension and load torque, which are the state of slack in the belt. This is due to the influence of noise generated by the electric motor 5, the belt 61, which is the power transmission mechanism 6, and the fan, which is the load equipment 7, and the resonance between these. Therefore, when selecting a single characteristic frequency effective for abnormality diagnosis, it is necessary to select the optimal order frequency taking these factors into consideration, otherwise it will be impossible to perform abnormality diagnosis with high accuracy. In other words, if abnormality diagnosis is performed by narrowing down the characteristic frequency to one, there is a risk that high accuracy of diagnosis cannot be expected.

[0061] 10A is a diagram illustrating the relationship between the belt tension and the feature amount when no load is applied, and FIG. 10B is a diagram illustrating the relationship between the belt tension and the signal strength (current value) of the -1st order belt rotation frequency as a comparative example. Note that the belt tension position of "starting to loosen" in each diagram corresponds to FIG. 8, and the belt tension position of "growing loosen" corresponds to the state in FIG. 4.

[0062] In FIG. 10A, the feature quantity C under no load (load torque 0) calculated by the abnormality diagnosis device 100 according to the first embodiment ｂ 1 increases when the tension is no longer adequate. ｂ The proper tension is normal, and the characteristic quantity C ｂ 1 is 0 or almost 0. Feature C ｂ As the value of 1 increases, the tension begins to deviate from the appropriate value, and the preset threshold value B ｔｈ When it exceeds 1a, it is determined that the loosening has begun. ｔｈ If the threshold value B exceeds 1b, it is determined that "inspection is required." ｔｈIf the value exceeds 1c, the looseness will progress and an abnormality will be detected. In this case, the abnormality diagnosis can be performed according to the looseness of the belt, i.e., the tension. ｂ By calculating 1, the belt tension at the time of calculation can be grasped and the occurrence of an abnormality can be predicted, and maintenance can be performed before the load device 10 stops due to a broken belt or the like.

[0063] FIG. 10B is a comparative example showing the relationship between belt tension and signal strength of the −1st order belt rotation frequency when the −1st order belt rotation frequency is used as an index for abnormality diagnosis. The area where the signal strength changes more significantly than when the tension is appropriate is the area shown by the ellipse. ｔｈ 0, indicating that the belt has become significantly loose. In the comparative example, an abnormality is determined when the belt reaches the region indicated by the ellipse. In other words, unlike the present embodiment, it is not possible to detect the change in the looseness. ｔｈ 0 is the threshold B in FIG. ｔｈ This corresponds to 1a, for example, 5 dB.

[0064] 11A is a diagram showing the relationship between the belt tension and the feature amount at the time of rated load, and FIG. 11B is a diagram showing the relationship between the belt tension and the signal strength (current value) of the −1st order belt rotation frequency as a comparative example. Note that the belt tension position where slack begins in each diagram corresponds to FIG. 9, and the belt tension position where slack progresses corresponds to the state in FIG. 5.

[0065] 11A also shows the feature quantity C at the time of rated load (load torque is rated) calculated by the abnormality diagnosis device 100 according to the first embodiment. ｂ 2 increases when the tension is no longer adequate. ｂ The characteristic quantity C2 starts to increase and increases as the loosening progresses. ｂ 2 is 0 or almost 0. Feature C ｂ When the value of 2 increases, the tension starts to deviate from the appropriate value, and the preset threshold value B ｔｈ When it exceeds 2a, it is determined that the loosening has begun. ｔｈ If the threshold value B exceeds 2b, it is determined that "inspection is required."ｔｈ If the looseness exceeds 2c, and it is determined to be an abnormality, the abnormality diagnosis can be determined according to the looseness of the belt, that is, the tension. ｂ By calculating 2, the belt tension at the time of calculation can be grasped and an abnormality can be predicted, and maintenance can be performed before the load device 10 stops due to a broken belt or the like.

[0066] 11B is a diagram showing the relationship between the belt tension and the signal strength of the −1st order belt rotation frequency when the −1st order belt rotation frequency is used as an index for abnormality diagnosis as a comparative example. As in the case of FIG. 10B, the area where the signal strength changes more significantly than when the tension is appropriate is the area shown by the ellipse, and the threshold B ｔｈ 0, indicating that the belt has become significantly loose. In the comparative example, an abnormality is determined when the belt reaches the region indicated by the ellipse. In other words, unlike the present embodiment, it is not possible to detect the change in the looseness. ｔｈ 0 is the threshold B in FIG. ｔｈ This corresponds to 2a, for example, 20 dB.

[0067] As described above, when abnormality diagnosis is performed using a feature that is the sum of the absolute values ​​of the difference between the signal strength of the rotational frequency at the time of diagnosis and the signal strength of the rotational frequency under normal conditions, it can be seen that the diagnostic accuracy is higher than in the comparative example that tracks changes in the strength of a single rotational frequency.

[0068] The rotational frequency signal strength changes during an abnormality because, when the rotor of the electric motor 5 vibrates, the gap between the rotor and the stator periodically changes, causing a change in the magnetic flux density between the rotor and the stator. The change in magnetic flux density causes a change in the current, and this change is reflected in the sidebands. When the belt 61 becomes loose, the looseness may cause a significant increase in the vibration, or the looseness may reduce the torque transmission efficiency and cause the vibration to decrease. Therefore, both increases and decreases in the rotational frequency signal strength due to an abnormality are expected. However, when an abnormality occurs or progresses to an abnormal state, a change occurs in the signal strength from a normal state. Therefore, if the absolute value of the difference, which represents the change, is summed up to multiple higher orders, the sum increases as the abnormality progresses. Therefore, by using the feature quantities according to this embodiment, it is possible to perform an abnormality diagnosis with high accuracy.

[0069] In the calculation of the feature amount, the difference between the normal and abnormal states is calculated for the signal strength of the first to third rotational frequencies in the above description, but this is not limited to this. It is also possible to calculate higher order components, or to select only the higher order components without selecting the lower order components. The spectrum peak related to the target rotational frequency is the power supply frequency f ｓ The peaks occur symmetrically on the high-frequency side and the low-frequency side. In this example, the differences in signal strength between both peaks were summed, but the feature amount may be calculated using the difference in signal strength between only the high-frequency side or the low-frequency side. Furthermore, in calculating the feature amount, the sum of the absolute values ​​of the differences between the normal and abnormal states of the rotation frequency signal strength was used, but the sum of the differences between the normal and abnormal states of the rotation frequency signal strength may also be used. In this case, although accuracy is expected to be lower, by carefully considering the conditions, such as using multiple specific peaks, it is possible to improve the diagnostic accuracy compared to the comparative example, which tracks changes in the intensity of a single rotation frequency.

[0070] <Setting the Threshold Used to Determine Abnormality> Next, a method for setting the threshold will be described. As described above, the feature value increases as belt slack progresses. Therefore, by setting a threshold and determining that an abnormality exists when the feature value exceeds the threshold, abnormality diagnosis becomes possible. As shown in FIGS. 10A and 11A, the threshold value can be determined from the relationship between the feature value and the belt tension, if known. However, the feature value is not uniquely determined due to various conditions. The size of each component of the load device, for example, the size of each pulley, belt length, and fan size, is not necessarily the same, the driving conditions change, and conditions such as the number of higher-order components to be taken in the rotation frequency also differ. Therefore, in many cases, a new setting is required. Below, a method for setting a threshold value that is independent of each condition will be described.

[0071] One method for setting the threshold is to acquire signal strength in a normal state multiple times and use its standard deviation. First, the signal strength in a normal state is calculated multiple times (q times). The frequency range for calculating the signal strength is the frequency range for calculating the feature, and the signal strength is calculated for the spectrum peak of the frequency for calculating the feature. In this case, as described above, it is the frequency corresponding to belt slack. For multiple signal strength data, the sum of the absolute values ​​of the differences between the signal strength acquired the first time and the signal strength acquired the second time is calculated at the frequency corresponding to belt slack. Similarly, the sum of the absolute values ​​of the differences between each of the signal strengths from the second time to the qth time is calculated. That is, data is acquired of the sum of the absolute values ​​of the differences between the signal strengths for q-1 times. The standard deviation σ of the data of the sum of the absolute values ​​of the differences between the signal strengths for this q-1 time is calculated, and the standard deviation σ is multiplied by a constant a to determine the threshold aσ. Therefore, if the feature is approximately the same magnitude as the variation in signal strength in a normal state, it is not determined to be abnormal. Alternatively, multiple thresholds a1σ, a2σ, etc. can be set by setting multiple constants a1, a2, etc. so that they increase sequentially. As the abnormality progresses, the feature amount increases, so an abnormality level is output according to the threshold value exceeded, such as abnormality level 1 at threshold a1σ and abnormality level 2 at threshold a2σ.

[0072] The frequency range for calculating the standard deviation σ, i.e., the order of rotational frequencies to be used, may be determined in advance to be consistent with the subsequent calculation of feature quantities, and the standard deviation may be calculated within the corresponding range. Alternatively, the standard deviation may be calculated within multiple ranges. When multiple normal state signal intensities are acquired, the multiple normal state signal intensities may be stored in the criterion storage unit 133a along with the standard deviation σ and thresholds a1σ, a2σ, ..., or only the signal intensity of the first spectrum may be stored. When calculating the difference from the signal intensity of the diagnostic spectrum, the signal intensity of the first spectrum or the signal intensities of other spectra may be used.

[0073] The above method is used to calculate the feature amount and determine an abnormality. If an abnormality is diagnosed, the diagnosis result is displayed on the output unit 140. The display content can be set to only show an abnormality when a threshold is exceeded, or, if multiple thresholds are set, to display the abnormality level according to the threshold. If the abnormality level is serious, the device may be stopped.

[0074] <Example Using Other Rotational Frequencies> In the above description, in a configuration in which the load equipment 7 is a fan, the power transmission mechanism 6 is a belt 61, and the electric motor 5 is a three-phase induction motor, an example has been described in which the target for abnormality diagnosis is an abnormality in the tension of the belt 61 of the power transmission mechanism 6, but the target for abnormality diagnosis does not have to be the belt 61. Since the belt 61 applies a large radial load to the electric motor 5 due to the belt connection, the signal strength of the rotational frequency including high-frequency components is large, making it suitable for abnormality diagnosis using the feature quantity obtained by the sum of absolute difference values ​​as an index. Meanwhile, harmonic components of the rotational frequency of the electric motor 5 and the load equipment 7 are also generated, and therefore similar abnormality diagnosis can be performed.

[0075] For example, the rotation frequency f of the electric motor 5 ｍ Using f = f ｓ ±nf ｍ Based on the spectrum peaks and signal intensities of the sidebands of the load equipment 7, the sum of the absolute values ​​of the differences in signal intensities of the harmonics up to the nth order when the motor 5 is normal and when it is abnormal can be used as a feature to determine whether the load equipment 7 is abnormal. ｌ Using f = f ｓ ±nfｌ Based on the spectrum peaks and signal intensities of the sidebands, the sum of the absolute values ​​of the differences in signal intensities of harmonics up to the nth order when the motor 5 is normal and when it is abnormal can be used as a feature to determine whether there is an abnormality.

[0076] Furthermore, even in the case of a load device 10 in which the electric motor 5 and the load equipment 7 are directly connected without using a power transmission mechanism 6 such as a belt 61, it is possible to perform abnormality diagnosis on each of the electric motor 5 and the load equipment 7. Furthermore, although the example of the belt 61 has been used as the target for abnormality diagnosis, it goes without saying that during abnormality diagnosis, spectral peaks caused by each of the electric motor 5, the power transmission mechanism 6, and the load equipment 7 can be distinguished and abnormality diagnosis can be performed simultaneously.

[0077] As described above, according to embodiment 1, the abnormality diagnosis device 100 for determining an abnormality in the load device 10 includes a current detector 120 for detecting the current of the electric motor 5, and an abnormality diagnosis unit 130 for performing FFT analysis of the detected current and determining an abnormality using the extracted spectral peaks. The abnormality diagnosis unit 130 includes a peak analysis unit 135c for analyzing the detected current using the power supply frequency of the electric motor 5 and the frequency of sidebands relative to the power supply frequency, and analyzing the extracted multiple spectral peaks by frequency, a frequency determination unit 135d for determining from the spectral peaks analyzed by frequency which of the electric motor 5, power transmission mechanism 6, and load equipment 7 of the load device 10 the frequency of the spectral peak is attributable to, and an electric motor frequency storage unit 133b, a power transmission mechanism frequency storage unit 133c, and a load equipment frequency storage unit 133d for storing the frequency and signal strength of the spectral peak attributable to any of the types of equipment included in the load device 10 when the load device 10 is normal, and an abnormality determination unit 135f. The abnormality determination unit 135f includes a load torque selection unit 135f1 that selects the load torque when the current is detected by the current detector 120 during abnormality diagnosis, a feature quantity calculation unit 135f2 that performs FFT analysis on the current detected during abnormality diagnosis, and for a plurality of diagnostic spectrum peaks whose types have been determined, reads out from the frequency storage unit the frequency and signal strength of the spectrum peak during normal operation corresponding to the load torque selected by the load torque selection unit and the determined type, and calculates the absolute value of the difference in signal strength from the normal state for each frequency of the plurality of diagnostic spectrum peaks, and uses the sum of these values ​​as a feature quantity, and a feature quantity determination unit 135f3 that compares a preset threshold value with the feature quantity to determine an abnormality in the load device 10. With this configuration, an abnormality is determined not based on a change in the signal strength of a single spectrum peak, but on the sum of the absolute values ​​of the difference from the normal state for a plurality of spectrum peaks, thereby enabling highly accurate abnormality diagnosis.

[0078] Furthermore, storing all of the time-series data of the detected currents and the analysis results in the abnormality diagnosis device 100 requires a large capacity memory. In this embodiment, diagnosis is performed using only the signal intensities of the power supply frequency, the rotational frequency of the electric motor, the rotational frequency of the power transmission mechanism, and the rotational frequency of the load equipment, so storing only the signal intensities of specific frequencies makes it possible to reduce the amount of memory required.

[0079] Embodiment 2. An abnormality diagnosis device 100 according to Embodiment 2 will now be described with reference to the accompanying drawings. FIG. 12 is a block diagram showing the configuration of the abnormality diagnosis unit 130 of the abnormality diagnosis device 100 according to Embodiment 2. This embodiment differs from FIG. 3 of Embodiment 1 in that the abnormality determination unit 135f includes a function information storage unit 135f4 and an abnormality degree calculation unit 135f5, and an abnormality degree storage unit 137 that stores and outputs the abnormality degree calculated by the abnormality degree calculation unit 135f5. In this embodiment, the abnormality degree is quantitatively evaluated based on the calculated feature value. As in Embodiment 1, the target is belt tension abnormality. As shown in FIGS. 10A and 11A, the feature value tends to increase as the belt looseness progresses. By fitting a function to the relationship between the feature value and the abnormality degree, a relationship between the feature value and the belt looseness is created, enabling quantitative evaluation. The following description will focus on the differences, and the same points as in Embodiment 1 will be omitted. Here, the abnormality degree can quantitatively indicate the degree of progression from normal to abnormal.

[0080] In FIG. 12, a function information storage unit 135f4 stores information on a function fitted to the degree of abnormality and the feature amount, and an abnormality degree calculation unit 135f5 calculates the degree of abnormality based on the fitted function.

[0081] This function information must be created in advance, and because it varies depending on the type of equipment in the rotating machinery system, such as belt length and pulley diameter, it must be acquired at each facility. One method is to set up test equipment for similar equipment and acquire data in advance, but another method is to acquire data accumulated when the motor is in operation.

[0082] For example, when the feature value increases and it is time to replace the belt, the tension is measured and the relationship between the feature value and tension is stored in memory. A fitting function is created based on this data. This method is effective when multiple rotating machinery systems of the same type are in operation in a factory or other facility, as measuring data from one unit can be used for the other equipment.

[0083] During fitting, the accuracy of fitting can be improved by selecting only data within a specific range. For example, in the case of belt tension, the feature value increases from normal on both the loose and tight sides of the belt relative to the appropriate tension. Therefore, by extracting data from only one of these ranges, fitting with a monotonically increasing function becomes possible. In this embodiment, the target is belt looseness, and only data below the appropriate tension is extracted and fitted.

[0084] Next, the function fitting method will be described. The type of function and creation method are not specified in function fitting. In this case, fitting is performed using the least squares method using a linear function. The fitted equation is stored in the function information storage unit 135f4. The fitted linear function is expressed as equation (7): y = kx + m ... (7) where k and m are fitting coefficients, y is the feature amount, and x is the degree of anomaly, and k and m are stored in the function information storage unit 135f4.

[0085] The quantitative evaluation of the degree of abnormality using the stored formula will be described. As in the first embodiment, the feature value C ｂ is calculated. The calculated feature value C ｂ Here, is defined as the feature quantity y. The degree of abnormality x for the feature quantity y is calculated from equation (7) as follows: x = (y - m) / k (8) The state of fitting the linear function is shown in Figure 13. In Figure 13, the degree of abnormality is the tension of the belt.

[0086] In this example, a linear function was selected as the function to be fitted, but to improve accuracy, other functions can be selected, or weighting each component when obtaining the absolute value sum can be used. When weighting each component and fitting with a linear function, the advantage is that the amount of calculation required for function fitting is reduced.

[0087] The calculated abnormality degree is stored in the abnormality degree storage unit 137 and is also output to the output unit 140. In the first embodiment, an abnormality determination is output only when an abnormality is diagnosed, but in the second embodiment, the abnormality degree may be output after a specific threshold value is reached. That is, the abnormality determination unit 135f of the first embodiment may further include a function information storage unit 135f4 and an abnormality degree calculation unit 135f5 so as to output both the abnormality determination and the abnormality degree. Furthermore, the abnormality degree may be output regardless of whether the abnormality is normal or abnormal.

[0088] Although the method for calculating the degree of abnormality has been described above in relation to the abnormality diagnosis of the belt tension, the degree of abnormality can be calculated in a similar manner in other abnormality determinations. For example, the degree of abnormality of the eccentricity may be calculated in the abnormality diagnosis of the electric motor 5.

[0089] In the first and second embodiments described above, the abnormality diagnosis device 100 includes a processor 1000 and a storage device 2000, as shown in FIG. 14 , which illustrates an example of hardware. Although the storage device is not shown, it includes a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory. Alternatively, a hard disk auxiliary storage device may be used instead of the flash memory. The processor 1000 executes a program input from the storage device 2000. In this case, the program is input to the processor 1000 from the auxiliary storage device via the volatile storage device. The processor 1000 may output data such as calculation results to the volatile storage device of the storage device 2000, or may store the data in the auxiliary storage device via the volatile storage device.

[0090] 15, the system may further include a communication device 3000. For example, if a plant monitoring device that collectively monitors the load devices 10 is provided in a plant where the load devices 10 are installed, the abnormality diagnosis result or the degree of abnormality determined by the abnormality diagnosis device 100 can be transmitted from the output unit 140 to the plant monitoring device.

[0091] Although the abnormality diagnosis device 100 has been described as having the hardware configuration shown in FIG. 14 or FIG. 15, the motor control device 110 and the abnormality diagnosis unit 130 may each have the hardware configuration shown in FIG. 14 or FIG. 15.

[0092] Although various exemplary embodiments and examples are described in this disclosure, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are anticipated within the scope of the technology disclosed in this specification. For example, this includes cases where at least one component is modified, added, or omitted, or where at least one component is extracted and combined with components of another embodiment.

[0093] 2: Power supply, 5: Electric motor, 6: Power transmission mechanism, 61: Belt, 7: Load equipment, 10: Load device, 100: Abnormality diagnosis device, 110: Motor control device, 111: Inverter, 112: Control unit, 120: Current detector, 130: Abnormality diagnosis unit, 131: Load device setting unit, 132: First storage unit, 133: Second storage unit, 133a: Judgment criterion storage unit, 133b: Motor frequency storage unit, 133c: Power transmission mechanism frequency storage unit, 133d: Load equipment frequency storage unit, 135: Calculation unit, 135a: Spectral analysis unit, 135b: Sideband analysis unit, 135c: Peak analysis unit, 135d: Frequency judgment unit, 135f: Abnormality judgment unit, 135f1: Load torque selection unit, 135f2: feature amount calculation unit, 135f3: feature amount determination unit, 135f4: function information storage unit, 135f5: abnormality degree calculation unit, 136: diagnosis result storage unit, 137: abnormality degree storage unit, 140: output unit, 1000: processor, 2000: storage device, 3000: communication device.

Claims

1. An abnormality diagnosis device for determining an abnormality in a load device including an electric motor and a load equipment connected to the electric motor, a current detector for detecting a current of the electric motor; an abnormality diagnosis unit that performs FFT analysis on the current detected by the current detector and determines an abnormality using an extracted spectrum peak, The abnormality diagnosis unit a peak analysis unit that performs analysis using a power supply frequency of the motor and frequencies of sidebands relative to the power supply frequency, and analyzes the extracted spectrum peaks by frequency; a frequency determination unit that determines, from the spectrum peak analyzed by the peak analysis unit, whether the frequency of the spectrum peak is attributable to the electric motor or the load equipment; a frequency storage unit that stores, when the load device is normal, the frequency and signal strength of a spectrum peak caused by the electric motor and the frequency and signal strength of a spectrum peak caused by the load equipment for each load torque of the electric motor; an abnormality determination unit; The abnormality determination unit a load torque selection unit that selects a load torque when a current is detected by the current detector during abnormality diagnosis; a feature quantity calculation unit that performs FFT analysis on the current detected during abnormality diagnosis, reads out from the frequency storage unit the frequencies and signal intensities of spectrum peaks in normal times corresponding to the load torque selected by the load torque selection unit and the determined type for a plurality of diagnostic spectrum peaks, calculates the absolute values ​​of the differences in signal intensity from normal times for each frequency for the plurality of diagnostic spectrum peaks, and uses the sum of the differences as a feature quantity; and a feature quantity determining unit that determines whether the load device has an abnormality by comparing the feature quantity with a preset threshold value.

2. The load device further includes a power transmission mechanism between the electric motor and the load equipment. The frequency determination unit determining, from the spectrum peak analyzed by the peak analysis unit, the frequency of the spectrum peak caused by any one of the electric motor, the power transmission mechanism, and the load equipment; The frequency storage unit 2. The abnormality diagnosis device according to claim 1, wherein when the load device is normal, the frequency and signal strength of a spectral peak caused by the electric motor, the frequency and signal strength of a spectral peak caused by the load equipment, and the spectral peak and signal strength of the power transmission mechanism are stored for each load torque of the electric motor.

3. the power transmission mechanism is a belt, The abnormality diagnosis device according to claim 2 , wherein the abnormality determination unit determines whether the tension of the belt is abnormal.

4. 4. The abnormality diagnosis device according to claim 1, wherein the plurality of diagnostic spectral peaks have spectral peaks of different orders, and the feature amount calculation unit calculates absolute values ​​of differences in signal intensity from a normal state for each frequency of the plurality of diagnostic spectral peaks, and calculates a sum of the absolute values ​​as the feature amount.

5. 4. The abnormality diagnosis device according to claim 1, wherein the abnormality determination unit further includes a function information storage unit and an abnormality degree calculation unit that calculates an abnormality degree indicating a degree of progression from normality to abnormality, and fits a correlation between the feature amount calculated by the feature amount calculation unit and the abnormality degree with a function stored in the function information storage unit, and the abnormality degree calculation unit calculates the abnormality degree corresponding to the feature amount based on the fitted function.

6. 6. The abnormality diagnosis device according to claim 5, wherein the function stored in the function information storage unit is a linear function.

7. 4. The abnormality diagnosis device according to claim 1, wherein the load torque of the electric motor is calculated based on the current detected by the current detector.

8. An abnormality diagnosis device for determining an abnormality in a load device including an electric motor and a load equipment connected to the electric motor, a current detector for detecting a current of the electric motor; an abnormality diagnosis unit that performs FFT analysis on the current detected by the current detector and determines an abnormality using an extracted spectrum peak, The abnormality diagnosis unit a peak analysis unit that performs analysis using a power supply frequency of the motor and frequencies of sidebands relative to the power supply frequency, and analyzes the extracted spectrum peaks by frequency; a frequency determination unit that determines, from the spectrum peak analyzed by the peak analysis unit, whether the frequency of the spectrum peak is attributable to the electric motor or the load equipment; a frequency storage unit that stores, when the load device is normal, the frequency and signal strength of a spectrum peak caused by the electric motor and the frequency and signal strength of a spectrum peak caused by the load equipment for each load torque of the electric motor; an abnormality determination unit; The abnormality determination unit a load torque selection unit that selects a load torque when a current is detected by the current detector during abnormality diagnosis; a feature quantity calculation unit that performs FFT analysis on the current detected during abnormality diagnosis, reads out from the frequency storage unit the frequencies and signal intensities of spectrum peaks in normal times corresponding to the load torque selected by the load torque selection unit and the determined type for a plurality of diagnostic spectrum peaks, calculates the absolute values ​​of the differences in signal intensity from normal times for each frequency for the plurality of diagnostic spectrum peaks, and uses the sum of the differences as a feature quantity; a function information storage unit; an abnormality degree calculation unit that calculates an abnormality degree indicating the degree of progression from normal to abnormal, an abnormality diagnosis device that fits a correlation between the feature amount calculated by the feature amount calculation unit and the degree of abnormality with a function stored in the function information storage unit, and the degree of abnormality calculation unit calculates the degree of abnormality corresponding to the feature amount based on the fitted function.

9. The load device further includes a power transmission mechanism between the electric motor and the load equipment. The frequency determination unit determining, from the spectrum peak analyzed by the peak analysis unit, the frequency of the spectrum peak caused by any one of the electric motor, the power transmission mechanism, and the load equipment; The frequency storage unit 9. The abnormality diagnosis device according to claim 8, wherein when the load device is normal, the frequency and signal strength of a spectrum peak caused by the electric motor, the frequency and signal strength of a spectrum peak caused by the load equipment, and the spectrum peak and signal strength of the power transmission mechanism are stored for each load torque of the electric motor.

10. the power transmission mechanism is a belt, The abnormality diagnosis device according to claim 9 , wherein the abnormality determination unit determines whether the tension of the belt is abnormal.

11. 11. The abnormality diagnosis device according to claim 8, wherein the diagnostic spectral peaks have spectral peaks of different orders, and the feature amount calculation unit calculates absolute values ​​of differences in signal intensity from a normal state for each frequency of the diagnostic spectral peaks, and determines a sum of the absolute values ​​as the feature amount.

12. The abnormality diagnosis device according to claim 8 , wherein the function stored in the function information storage unit is a linear function.

13. 1. An abnormality diagnosis method for determining an abnormality in a load device including an electric motor and a load facility connected to the electric motor, comprising: a first step of detecting a current in the motor; a second step of performing FFT analysis on the detected current to detect a spectrum peak; a third step of extracting a frequency of a sideband wave relative to a power supply frequency of the motor from the spectrum peak detected in the second step; a fourth step of determining whether the frequency of the spectrum peak is attributable to the electric motor or the load equipment by using the power supply frequency and the frequency of the extracted sideband; a fifth step of selecting a load torque of the motor when the current is detected in the first step; a sixth step of calculating, as a feature, a sum of absolute values ​​of differences between signal intensities of frequencies of the plurality of spectrum peaks determined for each type in the fourth step and signal intensities of frequencies of spectrum peaks attributable to the type in a normal state, which correspond to the type determined in the fourth step and to the load torque selected in the fifth step; a seventh step of comparing the feature amount calculated in the sixth step with a preset threshold value to determine whether an abnormality exists in the load device.

14. The load device further includes a power transmission mechanism between the electric motor and the load equipment.

14. The abnormality diagnosis method according to claim 13, wherein in the fourth step, it is determined whether the frequency of the spectrum peak is attributable to one of the electric motor, the power transmission mechanism, and the load equipment, using the power supply frequency and the frequency of the extracted sideband wave.

15. the power transmission mechanism is a belt, 15. The abnormality diagnosis method according to claim 14, wherein in the seventh step, an abnormality in tension of the belt is determined.

16. 16. The abnormality diagnosis method according to claim 13, wherein the sixth step includes calculating absolute values ​​of differences in signal intensity between a normal state and a frequency of the plurality of spectral peaks, and determining a sum of the absolute values ​​as the feature quantity.

17. 16. The abnormality diagnosis method according to claim 13, further comprising, after the sixth step, a step of fitting a relationship between the feature amount and the degree of abnormality with a preset function, and calculating the degree of abnormality indicating a degree of progression from normality to abnormality.

18. The abnormality diagnosis method according to claim 17, wherein the function used in the step of calculating the degree of abnormality is a linear function.

19. 16. The abnormality diagnosis method according to claim 13, wherein the frequency of a spectrum peak caused by the type in a normal state, its signal strength, and the load torque, which are used in the sixth step, are acquired in the first step to the fifth step and stored in advance.