Diagnosis device for electric motor and diagnosis system for electric motor

Abstract

A monitoring diagnosis unit (51) comprises: an input unit (51A) which inputs specifications for a power conversion device (20) and an electric motor (30); a detection unit (51C) which inputs a current detected by a current detector (41) and a voltage detected by a voltage detector (42); an analysis unit (51D) which performs spectrum analysis on the basis of the current inputted by the detection unit (51C) and acquires a driving frequency of the electric motor (30) and a current frequency spectrum; an arithmetic unit (51E) which calculates the slip of the electric motor (30) on the basis of the specifications from the input unit (51A), current and voltage data from the detection unit (51C), and the driving frequency from the analysis unit (51D), and identifies a characteristic frequency of the current frequency spectrum; and a determination unit (51F) which determines abnormality on the basis of the signal intensity of the characteristic frequency identified by the arithmetic unit (51E).

Description

Electric motor diagnostic device and electric motor diagnostic system

[0001] The present disclosure relates to a diagnostic device and a diagnostic system for an electric motor.

[0002] When diagnosing abnormalities in a motor driven by pulse-width modulation control using a power converter, many spectral peaks originating from the power converter exist in the frequency spectrum band used for abnormality diagnosis, compared to when the motor is driven by a commercial power source. If the frequencies of these spectral peaks overlap with the characteristic frequencies that detect abnormalities in the motor, this can lead to false detections by the diagnostic device.

[0003] To address this problem, for example, Patent Document 1 listed below theoretically calculates the noise predicted to be generated from the specification information of the electric motor and the power conversion device, and compares it with the spectral peaks extracted during abnormality diagnosis to avoid false detection.

[0004] That is, the diagnostic device of Patent Document 1 includes a detection unit that detects a current flowing through an electric motor, an analysis unit that performs frequency analysis of the current detected by the detection unit and outputs the analysis result, a determination unit that determines whether there is an abnormality in the electric motor based on the spectral peak of at least one sideband wave component of a modulated wave obtained from the analysis result, and a frequency setting unit that sets a noise frequency in the current in advance, and the determination unit estimates the presence or absence of noise interference at the spectral peak of the sideband wave component based on the frequency of the sideband wave component and the set noise frequency, and determines whether there is an abnormality in the electric motor.

[0005] Patent No. 6824494

[0006] In the prior art, the frequency setting unit theoretically calculates noise frequencies using the modulation wave frequency, carrier frequency, sampling frequency, and power supply frequency, and these frequencies are excluded from the frequencies used for abnormality diagnosis. However, there is a problem that unpredictable noise appears in actual measurements in addition to these theoretically calculated values.

[0007] The present disclosure discloses a technique for solving the above-described problems, and aims to provide an electric motor diagnostic device and an electric motor diagnostic system that can diagnose abnormalities in an electric motor with high accuracy even when theoretically unpredictable noise is present.

[0008] An electric motor diagnostic device according to the present disclosure is an electric motor diagnostic device that diagnoses an abnormality in at least one of an electric motor driven by power converted by a power conversion device and a power transmission mechanism connected to the electric motor, the diagnostic device comprising: a current detector that detects a current supplied from the power conversion device to the electric motor; a voltage detector that detects a voltage supplied from the power conversion device to the electric motor; and a monitoring and diagnosing unit that monitors the abnormality based on the current detected by the current detector and the voltage detected by the voltage detector, the monitoring and diagnosing unit comprising: an input unit that inputs specifications of the power conversion device and the electric motor; a detection unit that inputs the current detected by the current detector and the voltage detected by the voltage detector; an analysis unit that performs spectrum analysis based on the current input by the detection unit to obtain a current frequency spectrum and a drive frequency of the electric motor; and a calculation unit that calculates a slip of the electric motor based on the specifications input by the input unit, the current and voltage data input by the detection unit, and the drive frequency obtained by the analysis unit, and identifies a characteristic frequency of the current frequency spectrum that is an indicator of the abnormality based on the calculated slip. and a determination unit that determines the abnormality based on the signal strength of the characteristic frequency identified by the calculation unit.A diagnostic system for an electric motor according to the present disclosure is a system for diagnosing an abnormality in at least one of an electric motor driven by power converted by a power conversion device and a power transmission mechanism connected to the electric motor, the diagnostic system comprising: a measuring device; and a diagnostic device, the measuring device comprising: a current detector that detects a current supplied from the power conversion device to the electric motor; a voltage detector that detects a voltage supplied from the power conversion device to the electric motor; a detecting unit that inputs current data detected by the current detector and voltage data detected by the voltage detector; a first memory unit that stores the current data and voltage data input to the detecting unit; and a network output unit that outputs the current data and voltage data stored in the first memory unit to a network, the diagnostic device comprising: a network input unit that inputs data output from the network output unit via the network; an input unit that inputs specifications of the power conversion device and the electric motor; and a second memory unit that stores data input from the network input unit and data input from the input unit. The system includes an analysis calculation unit that performs a current spectrum analysis based on the data stored in the second memory unit to obtain a current frequency spectrum and a drive frequency of the electric motor, calculates a slip of the electric motor based on the specifications input by the input unit, the current and voltage data detected by the detection unit, and the drive frequency, and identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality based on the calculated slip, and a judgment unit that judges the abnormality based on the signal strength of the characteristic frequency identified by the analysis calculation unit.

[0009] According to the electric motor diagnostic device and electric motor diagnostic system of the present disclosure, even when theoretically unpredictable noise is present, it is possible to diagnose an abnormality in an electric motor with high accuracy.

[0010] 1 is a block diagram showing a schematic configuration of a power conversion device and an electric motor diagnostic device according to a first embodiment. FIG. 14A is a circuit configuration diagram showing a main circuit unit of the power conversion device according to the first embodiment. FIG. 15B is a diagram explaining an overview of the operation of a control circuit unit of the power conversion device according to the first embodiment. FIG. 16A is a diagram showing an example of a current frequency spectrum acquired when a normal electric motor is driven. FIG. 16B is a diagram showing an equivalent circuit per phase of a three-phase induction motor. FIG. 16C is a diagram showing an equivalent circuit per phase of a three-phase induction motor during a lock test. FIG. 16D is a diagram showing an example of a display of motor lock test results. FIG. 16E is a flowchart for calculating slip of an electric motor according to the first embodiment. FIG. 16F is a flowchart for calculating slip of an electric motor according to the first embodiment. FIG. 16F is a block diagram showing a configuration of a monitoring and diagnosing unit of the electric motor diagnostic device according to the first embodiment. FIG. 16F is a flowchart showing learning of the electric motor diagnostic device according to the first embodiment. FIG. 16G is a flowchart showing learning of the electric motor diagnostic device according to the first embodiment. FIG. 16H is a flowchart showing identification of a characteristic frequency of an electric motor according to the first embodiment. 1. A flowchart showing diagnosis by the electric motor diagnostic device according to embodiment 1. FIG. 2. A flowchart showing diagnosis by the electric motor diagnostic device according to embodiment 1. FIG. 3. A block diagram showing a schematic configuration of a power conversion device and an electric motor diagnostic device according to embodiment 2. FIG. 4. A flowchart showing identification of a characteristic frequency of an electric motor according to embodiment 2. FIG. 5. A diagram showing an example of a current frequency spectrum and a voltage frequency spectrum acquired when a normal electric motor is driven. FIG. 6. A flowchart showing learning by the electric motor diagnostic device according to embodiment 3. FIG. 7. A flowchart showing learning by the electric motor diagnostic device according to embodiment 3. FIG. 8. A flowchart showing diagnosis by the electric motor diagnostic device according to embodiment 3. FIG. 9. A flowchart showing diagnosis by the electric motor diagnostic device according to embodiment 3. FIG. 10. A block diagram showing a configuration of an electric motor diagnostic system according to embodiment 4. FIG. 11. A block diagram showing an electric motor diagnostic system according to embodiment 4.1 is a block diagram illustrating an example of a hardware configuration of a diagnostic device according to an embodiment.

[0011] Embodiment 1. Fig. 1 is a block diagram showing the schematic configuration of a power conversion device and a motor diagnostic device according to Embodiment 1. As shown in Fig. 1, a power conversion device 20 converts the frequency of a three-phase AC power supply 10 from a commercial power source or the like and supplies the converted power to a motor 30. A diagnostic device 50 detects at least two phases of the current supplied from the power conversion device 20 to the motor 30 using a current detector 41 and detects the three-phase voltage using a voltage detector 42. The diagnostic device 50 analyzes the detected current and voltage to detect an abnormality in the motor 30. The current detector 41 and the voltage detector 42 may be built into the power conversion device 20, built into the diagnostic device 50, or externally attached. The power conversion device 20 includes a main circuit unit 21 that converts the frequency of the power, a control circuit unit 22 for operating the main circuit unit 21, and a setting unit 23 that determines the settings of the control circuit unit 22. The diagnostic device 50 includes a monitoring and diagnosing unit 51 that monitors and diagnoses abnormalities in the electric motor 30 based on the current detected by the current detector 41 and the voltage detected by the voltage detector 42, and a display unit 52 that displays the results obtained by the monitoring and diagnosing unit 51. Note that a network output unit may be provided in the display unit 52 to remotely present this information to the user.

[0012] 2 is a circuit configuration diagram showing a main circuit section of the power conversion device according to the first embodiment. The main circuit section 21 includes a converter 21A, a smoothing capacitor 21B, and an inverter 21C. The converter 21A converts AC power from the AC power source 10 into DC power and stores it in the smoothing capacitor 21B. The inverter 21C converts the DC power stored in the smoothing capacitor 21B into AC power and supplies it to the electric motor 30. The converter 21A is configured as a three-phase bridge circuit having six diodes Da, and an input line for each phase is connected to the AC power source 10. The inverter 21C is configured as a three-phase bridge circuit having six switching elements Q, each with a diode connected in anti-parallel, and an output line for each phase is connected to the electric motor 30. The switching element Q may be, for example, an insulated gate bipolar transistor (IGBT) or a metal-oxide-semiconductor field effect transistor (MOSFET). The switching operation of the inverter 21C is controlled by a control signal generated by a control circuit 22. The operation of the control circuit 22 follows the operating conditions of the power conversion device 20 set by the user in a setting unit 23. The configurations of the converter 21A and the inverter 21C are not limited to those shown in the figure. Furthermore, while the main circuit 21 in FIG. 2 is shown as including the converter 21A and connected to the AC power source 10, the converter 21A may be omitted as long as it includes the inverter 21C that converts DC power to AC power and supplies the power to the electric motor 30.

[0013] FIG. 3 is a diagram illustrating an overview of the operation of the control circuit unit 22 of the power conversion device 20 according to the first embodiment. The control circuit unit 22 of the power conversion device 20 drives the main circuit unit 21 of the power conversion device 20 using pulse width modulation. The pulse width modulation method modulates the frequency of the power supplied to the electric motor 30 by cutting out the DC voltage of the smoothing capacitor 21B at short-time intervals. The short-time intervals are cut out by rapidly controlling the ON / OFF of the switching element Q of the inverter 21C. The control signal G that controls the switching element Q is generated by the modulated wave generating unit 22A, signal discretizing unit 22B, and signal extracting unit 22C of the control circuit unit 22. The modulated wave generating unit 22A oscillates a sine wave at a drive frequency f0 input by the user. The signal discretizing unit 22B samples the modulated wave output by the modulated wave generating unit 22A at a frequency fs to obtain discrete data. The control circuit 22 compares the discrete data from the signal discretization unit 22B with a carrier frequency fc (for example, a triangular wave) generated by the signal extraction unit 22C to generate a control signal G.

[0014] In the process of generating the control signal G shown in Figure 3, noise occurs at a frequency that is the greatest common divisor of the drive frequency f0, sampling frequency fs, and carrier frequency fc, i.e., an integer multiple of GCD (f0, fs, fc). This noise is defined as Fpwm as shown in equation (1). GCD is an abbreviation for Greatest Common Divisor.

[0015] Fpwm=i・GCD(f0, fs, fc) (1)

[0016] where i is a positive integer. For example, when f0 = 70 Hz, fs = 4000 Hz, and fc = 2000 Hz, GCD(f0, fs, fc) = 10 Hz, and Fpwm is 10 Hz, 20 Hz, 30 Hz, ...

[0017] In addition to the above-mentioned Fpwm, noise that falls within the frequency band monitored by the diagnostic device 50 also occurs when the converter 21A of the power conversion device 20 generates a DC voltage. This noise is defined as Fv as shown in equation (2).

[0018] Fv=|m・fac±n・f0| (2)

[0019] where fac is the frequency of the power supplied to the converter 21A, and m and n are positive integers. For example, if f0=70 Hz and fac=60 Hz, then fv will be 10 Hz, 20 Hz, 30 Hz, and so on.

[0020] Furthermore, noise that is an integer multiple of the modulated wave is generated as a harmonic of the modulated wave, which is defined as F0 as shown in equation (3).

[0021] F0=k·f0 (3) where k is a positive integer.

[0022] Other noises generated by the power conversion device 20 include noise caused by the dead time provided to protect the switching element Q from damage when generating the control signal G, noise caused by the effect of overmodulation that occurs depending on the amplitude conditions of the modulated wave and carrier wave, and noise generated when extracting the control signal G. The frequencies of these noises and the characteristic frequencies that indicate an abnormality in the electric motor 30 monitored by the diagnostic device 50 must be correctly identified to prevent erroneous detection.

[0023] Returning to FIG. 1 , the current detector 41 detects the current generated by the power conversion device 20. The detected current is processed by the diagnostic device 50. The diagnostic device 50 first learns the state in which the motor 30 is operating normally. In other words, it performs spectrum analysis of the current when the motor 30 is operating normally and stores its characteristics. When performing a diagnosis, it compares the current characteristics with the learned current characteristics. If an abnormality occurs in the motor 30, an abnormality will appear in the frequency corresponding to each abnormal mode. Here, as an example, a mechanical abnormality and rotor bar damage in the motor 30 will be described.

[0024] [Mechanical System Abnormality] When an abnormality occurs in the mechanical system of the electric motor 30, vibration and eccentricity of the rotor occur. This eccentricity causes the gap length between the rotor and the stator to fluctuate periodically. The periodic fluctuation in the gap length changes the electrical properties of the electric motor 30, causing slight disturbances in the current detected by the current detector 41. If this disturbance is analyzed spectrally, sidebands f0±fm' of the drive frequency f0 can be confirmed. Here, fm' is as shown in equation (4).

[0025] fm' = ((1 - s) / p) f0 (4) where p is the number of pole pairs and s is the slip.

[0026] [Rotor Bar Damage] When damage occurs to the rotor bars of the electric motor 30, a negative-phase component (-s·f0) is generated in the current in the rotor bars. This negative-phase component returns a current with a frequency of (1-s)·f0 in the stator. This current generates torque oscillations with a frequency of 2s·f0, inducing magnetic flux oscillations of (1±2·s)·f0. As a result, slight disturbances occur in the current detected by the current detector 41. Spectral analysis of this disturbance reveals sidebands f0±fr' of the drive frequency f0, where fr' is as shown in equation (5).

[0027] fr'=2・s・f0 (5)

[0028] The slip s is defined by the rotation speed N0 of the rotating magnetic field and the rotation speed Nr of the rotor as shown in equation (6).

[0029] s=(N0-Nr) / N0 (6)

[0030] The rotation speed N0 of the rotating magnetic field is defined as shown in equation (7) using the number of pole pairs p and the drive frequency f0.

[0031] N0=(60 / p)f0 (7)

[0032] When diagnosing mechanical system abnormalities and rotor bar damage in the electric motor 30, the signal strength of each characteristic frequency shown by equations (4) and (5) is stored in advance as a normal value from the frequency spectrum obtained from the measurement values ​​of the current detector 41. When an abnormality occurs in the electric motor 30, the signal strength of the characteristic frequency corresponding to each abnormal mode increases. When diagnosing an abnormality in the electric motor 30, the diagnostic device 50 compares the signal strength of each characteristic frequency with the normal value and diagnoses an abnormality in the electric motor 30 by detecting an increase in signal strength.

[0033] To achieve the above function, the diagnostic device 50 needs to correctly extract and monitor characteristic frequency components from the frequency spectrum of the electric motor 30. However, when the electric motor 30 is driven by the power conversion device 20, various noises appear in the frequency spectrum, as described above. For example, if the AC power supply 10 supplies AC power of fac = 60 Hz to the power conversion device 20, and the power conversion device 20 supplies AC power of f0 = 70 Hz to the electric motor 30, noise is generated at 10 Hz intervals according to equation (2).

[0034] Figure 4 shows an example of a current frequency spectrum obtained when a normal electric motor is driven under the conditions of fac = 60 Hz, f0 = 70 Hz, and a load factor of 100%. It can be seen that noise occurs at 10 Hz intervals, as shown in equation (2), with the drive frequency of f0 = 70 Hz as the main component. As shown in Figure 4, each actual noise component appears with a certain degree of frequency width, and the intervals between each noise component are not smooth, resulting in multiple spectral peaks.

[0035] If the motor 30 is normal, the characteristic frequencies of mechanical system abnormalities and rotor bar damage have relatively low signal strengths, so there is a possibility that the diagnostic device 50 may mistakenly extract noise components as the characteristic frequency components of the motor 30. To eliminate this possibility, it is necessary to accurately extract the characteristic frequencies even in an environment where a lot of noise is generated, as shown in Figure 4. To achieve the above objective, it is important to note that the characteristic frequency is a function of the slip s, as shown in equations (4) and (5). The drive frequency f0 can be estimated by extracting the principal component in the frequency spectrum, and p can be determined from the specifications of the motor 30.

[0036] [Calculating Slip] To derive a formula for calculating slip s, an equivalent circuit per phase of the motor 30 is shown in FIG. 5. In FIG. 5, x1 and r1 represent the reactance and resistance of the stator of the motor 30, x2 and r2 represent the reactance and resistance of the rotor of the motor 30, and s represents slip. Furthermore, g0 represents the excitation conductance, b0 represents the excitation susceptance, and Y0 represents the excitation admittance. The subscripts 0, 1, and 2 represent the excitation circuit, stator, and rotor of the motor, respectively. It should be noted that, when purchasing the motor 30, data on the results of a lock test and stator winding resistance are generally available as characteristic test results. The results of a lock test assuming no slip (s = 1) generally include the items shown in FIG. 7.

[0037] The process of deriving slip s using these items will be described below. In a lock test, the applied voltage is generally set low, so the excitation circuit shown in Figure 5 is ignored, and the rotor is fixed, so slip s = 1, and therefore the equivalent circuit during a lock test is approximated as shown in Figure 6. In the equivalent circuit of Figure 6, r1 is the stator winding resistance shown in the characteristics test results. Rotor resistance r2 can be expressed using r1 and the power consumption W and current I1 shown in Figure 7, as shown in equation (8).

[0038] r2=(W / 3(I1) ２ ) -r1 (8)

[0039] The reactance X can be calculated from FIG. 6 as shown in equation (9).

[0040]

[0041] Using these results, slip s is found from the equivalent circuit shown in Figure 5. To do this, first, attention is paid to the power consumption of the induction machine, which can be expressed as in equation (10).

[0042]

[0043] On the other hand, it is known that torque is generally expressed as in equation (11).

[0044]

[0045] The slip s can be calculated from equations (11) and (10) as shown in equation (12).

[0046]

[0047] If I2 is expressed using X from equation (9) assuming I1 = I2, it becomes as shown in equation (13), where R = r1 + r2 + r2(1 - s) / s.

[0048]

[0049] By solving this for s, we can calculate the slip. ２ If = 0, the slip is as shown in equation (14).

[0050]

[0051] The stator winding resistance r1 is recorded in the characteristic test results, and the stator winding resistance r2 can be calculated from equation (8). The voltage V1 is obtained by converting the time waveform of the voltage detected by the voltage detector 42 shown in FIG. 1 into an effective value, and the drive frequency f0 can be calculated by frequency-converting the measurement value of the current detector 41 and extracting the principal component. The torque T can be calculated from the measurement results of the current detector 41 and the voltage detector 42. An overview of these is provided below.

[0052] It is known that torque T can generally be expressed as in equation (15) using αβ-transformed stator current I, interlinkage magnetic flux φ, and the number of pole pairs p.

[0053]

[0054] where the subscripts α and β indicate the α-axis and β-axis, respectively. From the definition of the interlinkage magnetic flux φ in equation (15), it can be calculated from the stator voltage V, resistance r1, and current I as shown in equation (16).

[0055]

[0056] The αβ transformation for current is as shown in equation (17).

[0057]

[0058] Here, the three-phase currents are in a balanced state (Iu + Iv + Iw = 0), and the u-phase and v-phase currents are shown as having been subjected to αβ conversion as an example. These phase currents are measured by a current detector 41.

[0059] The αβ conversion for voltage is as shown in equation (18).

[0060]

[0061] The right side of equation (18) is obtained by converting the phase voltages Vu, Vv, and Vw detected by the voltage detector 42 into line voltages Vuv=Vu-Vv, Vvw=Vv-Vw.

[0062] When calculating torque T, the two-phase current obtained by the current detector 41 is αβ-converted using equation (17), and the three-phase voltage obtained by the voltage detector 42 is αβ-converted using equation (18). These αβ-converted currents and voltages are used in equation (16) to calculate the flux linkage. Then, the αβ-converted currents and voltages and the flux linkage are used in equation (15) to calculate torque T. Note that, in order to calculate torque T with high accuracy, a digital filter that removes DC components may be applied in the integral calculation performed in equation (16), or a process of removing iron loss from the calculated torque T may be performed using the equivalent circuit shown in FIG. 5.

[0063] The torque T obtained by the above procedure is used in equation (14) to calculate the slip s.

[0064] 8 and 9 are flowcharts for calculating the slip of the electric motor according to the first embodiment, summarizing the slip calculation procedure described above. In step S101, the current time waveform is acquired by the current detector 41. In step S102, the current time waveform acquired by the current detector 41 is frequency converted. In step S103, the drive frequency f0 is extracted from the current frequency waveform. In addition, in step S104, the current detected by the current detector 41 is αβ converted using equation (17).

[0065] In step S105, the voltage time waveform is acquired by the voltage detector 42. In step S106, the voltage detected by the voltage detector 42 is subjected to αβ conversion using equation (18). In step S107, the flux linkage φ is calculated using equation (16) based on the result of the αβ conversion of the current in step S104 and the result of the αβ conversion of the voltage in step S106. In step S108, the torque T is calculated using equation (15) based on the result of the αβ conversion of the current in step S104 and the flux linkage φ calculated in step S107. In step S109, the voltage detected by the voltage detector 42 is converted to an effective value, and in step S110, the voltage V1 is calculated.

[0066] In step S120, the characteristic test results are acquired by the input unit 51A of the monitoring and diagnosing unit 51, which will be described later. The characteristic test results include the stator winding resistance r1 as shown in step S121, as well as the input power W and current I1 from the constraint test results. Therefore, in step S122, the rotor winding resistance r2 can be calculated using equation (8).

[0067] Then, in step S130, the slip s is calculated using equation (14) based on the drive frequency f0 extracted in step S103, the torque T calculated in step S108, the voltage V1 calculated in step S110, the stator winding resistance r1 acquired in step S121, and the rotor winding resistance r2 calculated in step S122.

[0068] [Configuration of Monitoring and Diagnosis Unit] Figure 10 is a block diagram showing the configuration of the monitoring and diagnosis unit of the electric motor diagnostic device according to the first embodiment. In Figure 10, the monitoring and diagnosis unit 51 includes an input unit 51A, a detection unit 51C, a memory unit 51B, an analysis unit 51D, a calculation unit 51E, and a determination unit 51F. The input unit 51A inputs specifications (data) of the electric motor and the power conversion device. The detection unit 51C is connected to the current detector 41 and the voltage detector 42 to detect the current and voltage supplied to the electric motor 30. The memory unit 51B stores the specifications (data) input by the input unit 51A, stores analysis data analyzed by the analysis unit 51D, and stores information calculated by the calculation unit 51E. The analysis unit 51D analyzes the time waveform of the current detected by the detection unit 51C to calculate a frequency spectrum and extracts the drive frequency with the highest signal strength from the calculated frequency spectrum. The calculation unit 51E calculates the slip of the electric motor 30 based on the data input by the input unit 51A and the current and voltage data detected by the detection unit 51C, and identifies a characteristic frequency of the frequency spectrum indicating an abnormality based on the calculated slip. The determination unit 51F determines an abnormality in the electric motor based on the signal strength of the characteristic frequency identified by the calculation unit 51E.

[0069] [Learning Phase] When the monitoring and diagnosing unit 51 of the diagnosing device 50 detects an abnormality in the electric motor 30, the monitoring and diagnosing unit 51 first drives a normal electric motor 30 to learn the characteristic frequencies of the current frequency spectrum and their signal strengths.

[0070] 11 and 12 are learning flowcharts of the electric motor diagnostic device according to embodiment 1. First, in step S201, a user inputs specifications (data) of the electric motor 30 and the power conversion device 20 to the input unit 51A of the monitoring and diagnosing unit 51. These specifications (data) include the number of pole pairs p of the electric motor 30, the results of a characteristic test, and the input power frequency fac, carrier frequency fc, and sampling frequency fs for the power conversion device 20. These input specifications (data) are stored in the memory unit 51B.

[0071] Next, in step S202, the detection unit 51C acquires the time waveforms of the current and voltage supplied from the power conversion device 20 to the electric motor 30, which are detected by the current detector 41 and the voltage detector 42.

[0072] Next, in step S203, the analysis unit 51D extracts a current frequency spectrum by performing spectrum analysis on the time waveform of the current detected by the current detector 41. Then, in step S204, the analysis unit 51D extracts the drive frequency f0 with the highest signal strength from the current frequency spectrum and stores it in the memory unit 51B.

[0073] Next, in step S205, the calculation unit 51E acquires the drive frequency f0, the input power frequency fac for the power conversion device 20, the carrier frequency fc, and the sampling frequency fs stored in the memory unit 51B, and calculates the noise frequency by using the above-mentioned equations (1), (2), and (3). The calculated noise frequency is stored in the memory unit 51B.

[0074] In step S203, the time waveform of the voltage detected by the voltage detector 42 is analyzed by the analyzer 51D, and an effective value is calculated. The calculated effective value is stored in the memory 51B. Furthermore, the time waveform of the current detected by the current detector 41 and the time waveform of the voltage detected by the voltage detector 42 are subjected to αβ conversion by the analyzer 51D, and the torque T is calculated using equation (15). The calculated torque T is stored in the memory 51B.

[0075] Then, in step S206, the drive frequency f0, torque T, effective voltage V1, stator winding resistance r1, and rotor winding resistance r2 stored in memory unit 51B are sent to calculation unit 51E, which then calculates slip s using equation (14). The calculated slip s is stored in memory unit 51B.

[0076] Next, in step S207, the calculation unit 51E acquires the slip s, the drive frequency f0, and the number of pole pairs p of the electric motor 30 stored in the memory unit 51B, calculates the characteristic frequencies of the mechanical system abnormalities and the rotor bar damage from the above-mentioned equations (4) and (5), and stores them in the memory unit 51B.

[0077] Next, in step S208, the characteristic frequencies of the mechanical system abnormalities and rotor bar damage stored in memory unit 51B are sent to analysis unit 51D, and each characteristic frequency is identified from the frequency spectrum of the current analyzed in analysis unit 51D.

[0078] FIG. 13 is a flowchart showing how to identify a characteristic frequency of the electric motor according to the first embodiment, and FIGS. 14A and 14B, and FIGS. 15A and 15B show examples in which a characteristic frequency component is actually identified from the current frequency spectrum shown in FIG. 4 using the flowchart of FIG. 13.

[0079] As shown in Fig. 13, in step S301, the calculated value of slip s in step S206 in Fig. 11 is read. Then, when identifying the characteristic frequency, the characteristic frequency of the mechanical system abnormality is first identified (step S302). The reason for this is that, when comparing equations (4) and (5), the characteristic frequency of the mechanical system abnormality in equation (4) includes the number of pole pairs p of the electric motor 30. In other words, although the calculated slip s necessarily contains an error, in the case of a mechanical system abnormality, the error is multiplied by (1 / p) relative to the drive frequency f0, and therefore the influence of the error is less when identifying the characteristic frequency than in the case of rotor bar damage.

[0080] Under the conditions of the current frequency spectrum shown in Figure 4, the slip s was calculated to be 3.97% from equation (14). This slip s is applied to the flowchart shown in Figure 13 to first identify the characteristic frequency of the mechanical system anomaly. Applying the calculated slip s to equation (4) yields fm' = 33.61 Hz. Therefore, the calculated characteristic frequencies of the mechanical system anomaly are 36.39 Hz and 103.61 Hz.

[0081] Next, in step S303, the calculated slip is corrected. First, the calculated feature frequency is compared with the frequency spectrum of the current analyzed by the analysis unit 51D. As shown in FIGS. 14A and 14B, frequency components with maximum signal strength are extracted from the vicinity of the calculated feature frequency (e.g., a range of approximately ±0.5 Hz). FIG. 14A shows the frequency spectrum near the lower feature frequency of the mechanical anomaly. A spectral peak can be observed at 36.62 Hz near the calculated value of the lower feature frequency (Lfcv1) of 36.39 Hz. Therefore, this spectral peak of 36.62 Hz is designated as the specific value of the lower feature frequency (Lfsv1). FIG. 14B shows the frequency spectrum near the upper feature frequency of the mechanical anomaly. A spectral peak can be observed at 103.64 Hz near the calculated value of the upper feature frequency (Ufcv1) of 103.61 Hz. Therefore, this spectrum peak of 103.64 Hz is set as the specific value (Ufsv1) of the upper characteristic frequency. These specific characteristic frequencies are sufficiently close to the true values ​​of the lower characteristic frequency (Lftv1 = 36.48 Hz) and the upper characteristic frequency (Uftv1 = 103.52 Hz), respectively. The true values ​​are characteristic frequencies of the mechanical system abnormality obtained from the slip s calculated by using the actual rotational speed of the electric motor 30 observed by a tachometer in equation (6).

[0082] Through this process, the characteristic frequencies of the mechanical system anomalies were identified. In the examples of Figures 14A and 14B, the characteristic frequencies of the mechanical system anomalies were identified as 36.62 Hz and 103.64 Hz. Using these values ​​in equation (4) and back-calculating the slip s, we obtain 4.43%. Although this value differs from the slip s of 3.97% calculated using equation (14), it is considered a value corrected from actual measurements and will be used when identifying the characteristic frequencies of rotor bar damage, as described below.

[0083] Next, in step S304, the characteristic frequency of the rotor bar damage is identified. Here, the characteristic frequency of the rotor bar damage is identified from the slip s of 4.34% corrected in step S303. Applying the corrected slip s to equation (5) yields fr' = 6.08 Hz. Therefore, the calculated characteristic frequencies of the rotor bar damage are 63.92 Hz and 76.08 Hz. These calculated characteristic frequencies are compared with the frequency spectrum of the current analyzed by analysis unit 51D.

[0084] As shown in Figures 15A and 15B, the frequency component with the maximum signal strength is extracted from the vicinity of the calculated feature frequency (for example, a range of approximately (+ / -) 0.5 Hz). Figure 15A shows the frequency spectrum near the lower feature frequency of the rotor bar damage. A spectral peak can be confirmed at 64.09 Hz near the calculated value of the lower feature frequency (Lfcv2), 63.92 Hz. Therefore, this spectral peak of 64.09 Hz is set as the specific value of the lower feature frequency (Lfsv2). Figure 15B shows the frequency spectrum near the upper feature frequency of the rotor bar damage. A spectral peak can be confirmed at 76.05 Hz near the calculated value of the upper feature frequency, 76.08 Hz (Ufcv2). Therefore, this spectral peak of 76.05 Hz is set as the specific value of the upper feature frequency (Ufsv2). These identified characteristic frequencies are sufficiently close to the true value of the lower characteristic frequency (Lftv2 = 64.07 Hz) and the true value of the upper characteristic frequency (Uftv2 = 75.93 Hz), respectively. The true values ​​are characteristic frequencies of rotor bar damage obtained from the slip s calculated using the actual rotational speed of the motor 30 observed with a tachometer in equation (6).

[0085] 11 and 12. In step S211, the analysis unit 51D compares the characteristic frequency identified in step S208 stored in the memory unit 51B with the noise frequency range calculated in step S205 using equations (1), (2), and (3), and determines whether the identified characteristic frequency is included in the noise frequency range. The noise frequency range is defined by uniformly specifying, for example, (+ / -) 1 Hz for the noise frequencies calculated using equations (1), (2), and (3) (step S210).

[0086] In step S211, if the feature frequency identified in step S208 is not within the noise frequency range specified in step S210, the process proceeds to step S212, where those feature frequencies and their signal intensities are learned as normal values ​​along with the drive frequency. During learning, it is not necessary for both the upper and lower sidebands of the feature frequency to be outside the noise frequency range. If one side is within the noise frequency range, the signal intensity of only the other side may be learned. Furthermore, if both the upper and lower sidebands of the feature frequency are outside the noise frequency range, the average value of both signal intensities may be defined as the normal value, or only one of the signal intensities may be learned, taking into account the respective physical laws and environmental conditions. Learning the normal value involves intermittently acquiring signal intensities over a set period, such as several weeks, and then applying statistical processing such as averaging and variance to define the normal value range.

[0087] In step S211, if all the characteristic frequencies identified in step S208 are included in the range of noise frequencies designated in step S210, the process proceeds to step S213, where it is determined that diagnosis is not possible.

[0088] When the learning period of the electric motor diagnostic device described with reference to FIGS. 11 and 12 ends, the drive frequency f0 of the electric motor 30, the identification of the characteristic frequency, and the normal value range of the signal strength of the identified characteristic frequency are acquired.

[0089] [Diagnostic Phase] Subsequently, diagnosis of the electric motor diagnostic device is performed as shown in the flowcharts of Figures 16 and 17. In the flowcharts of Figures 16 and 17, first, in step S501, learning data is read. Here, the specific frequency learned in the learning flowcharts of Figures 11 and 12, the normal value range of its signal strength, and the drive frequency at that time are read. Subsequently, the following steps are performed: a current and voltage detection step in step S502, an analysis step in step S503, a drive frequency extraction step in step S504, a noise frequency calculation step in step S505, a slip calculation step in step S506, a feature frequency calculation step in step S507, a feature frequency identification step in step S508, a noise frequency range designation step in step S510, and a determination step of whether the feature frequency is included in the noise frequency range in step S511. These steps are similar to the step S202 of detecting current and voltage, the step S203 of analyzing, the step S204 of extracting a drive frequency, the step S205 of calculating a noise frequency, the step S206 of calculating a slip, the step S207 of calculating a feature frequency, the step S208 of identifying a feature frequency, the step S210 of specifying a noise frequency range, and the step S211 of determining whether or not the feature frequency is included in the noise frequency range, which are performed in the flowcharts of FIGS. 11 and 12 .

[0090] If, in step S511, the feature frequency is within the noise frequency range, the process proceeds to step S515, where diagnosis is deemed impossible and a message indicating this is displayed on the display unit 52. If, in step S511, the feature frequency is not within the noise frequency range, the process proceeds to step S512, where it is determined whether the signal strength of the feature frequency is outside the normal range. If, in step S512, the signal strength of the feature frequency is not outside the normal range, the process returns to step S502, where the diagnosis continues. If, in step S512, the signal strength of the feature frequency is outside the normal range, the process proceeds to step S513, where the location of the abnormality is identified. Then, in step S514, an alarm and the identified location of the abnormality are displayed on the display unit 52.

[0091] As described above, according to the first embodiment, there is provided an electric motor diagnostic device for diagnosing an abnormality in at least one of an electric motor driven by power converted by a power conversion device and a power transmission mechanism connected to the electric motor, the diagnostic device comprising: a current detector for detecting a current supplied from the power conversion device to the electric motor; a voltage detector for detecting a voltage supplied from the power conversion device to the electric motor; and a monitoring and diagnosing unit for monitoring the abnormality based on the current detected by the current detector and the voltage detected by the voltage detector, the monitoring and diagnosing unit comprising: an input unit for inputting specifications of the power conversion device and the electric motor; a detection unit for inputting the current detected by the current detector and the voltage detected by the voltage detector; an analysis unit for performing spectrum analysis based on the current input by the detection unit and acquiring a current frequency spectrum and a drive frequency of the electric motor; and a calculation unit for calculating a slip of the electric motor based on the specifications input by the input unit, the current and voltage data input by the detection unit, and the drive frequency acquired by the analysis unit, and for identifying a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality based on the calculated slip. and a determination unit that determines the abnormality based on the signal strength of the characteristic frequency identified by the calculation unit. Therefore, the diagnostic device can accurately identify the characteristic frequency based on a slip calculation method that uses current and voltage measurements, making it possible to provide a diagnostic device for an electric motor with few false positives.

[0092] Furthermore, during learning before the diagnostic device performs the diagnosis of the abnormality, the diagnostic device drives the electric motor while it is functioning normally, identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality by calculating a slip of the electric motor, and stores the identified characteristic frequency, the signal strength of the identified characteristic frequency, and the drive frequency of the electric motor obtained by the analysis unit as normal data; and during diagnosis when the diagnostic device performs the diagnosis of the abnormality, the diagnostic device drives the electric motor, identifies a new characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality by calculating a slip of the electric motor, and determines the abnormality by comparing the newly identified characteristic frequency, the signal strength of the newly identified characteristic frequency, and the drive frequency of the electric motor newly obtained by the analysis unit with the normal data, thereby providing a diagnostic device for an electric motor that can diagnose an abnormality with high accuracy.

[0093] Furthermore, the calculation unit specifies a noise frequency range for the power conversion device based on the drive frequency acquired by the analysis unit and the specifications of the power conversion device input by the input unit, and the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the calculation unit, so that a highly accurate motor diagnostic device that can effectively remove the noise frequencies can be provided.

[0094] Furthermore, the characteristic frequency identified based on the slip calculated by the calculation unit is corrected based on the waveform of the current frequency spectrum acquired by the analysis unit, so that it is possible to provide a diagnosis device for an electric motor that can identify a characteristic frequency with high accuracy.

[0095] Second Embodiment Fig. 18 is a block diagram showing the general configuration of a power conversion device and a motor diagnostic device according to a second embodiment. As shown in Fig. 18, a power conversion device 20 converts the frequency of AC power from an AC power supply 10 and supplies the converted power to an electric motor 30. The electric motor 30 is connected to a load 70 via a power transmission mechanism 60. A diagnostic device 50 detects at least two phases of the current supplied from the power conversion device 20 to the electric motor 30 using a current detector 41, and detects three-phase voltages using a voltage detector 42, and detects an abnormality in the electric motor 30 by analyzing the detected currents and voltages. The other configurations are the same as those shown in Fig. 1 for the first embodiment, and therefore description thereof will be omitted.

[0096] In the first embodiment, the diagnostic device 50 diagnoses the electric motor 30 for mechanical abnormalities and rotor bar damage, whereas in the second embodiment, the diagnostic device 50 also diagnoses the power transmission mechanism 60 connected to the electric motor 30. It should be noted that the diagnosis of the electric motor 30 for mechanical abnormalities and rotor bar damage described in the first embodiment and the diagnosis of the power transmission mechanism 60 of the electric motor 30 described in the second embodiment can be easily combined. The following description will focus on the differences from the first embodiment, and as other points are the same as those in the first embodiment, a description thereof will be omitted.

[0097] 18 , the electric motor 30 is connected to a load 70 via a power transmission mechanism 60. In the power transmission mechanism 60, for example, a belt 61 connects a pulley Pu1 attached to the rotating shaft of the electric motor 30 with a pulley Pu2 attached to the rotating shaft of the load, thereby transmitting the power of the electric motor 30 to the load 70. In this case, if the radius of the pulley Pu1 is Dr, the length of the belt 61 is L, and the rotation speed of the electric motor 30 is Nr, the length of the belt rotated by the pulley Pu1 per unit time is 2π·Dr·Nr. From the ratio of this length to the total length L of the belt, the rotation frequency fb′ of the belt can be calculated according to equation (19).

[0098] fb'=((2π・Dr) / L)・fm' (19)

[0099] where fm' is the rotational frequency of the electric motor 30 and is defined via the through slip s in equation (4). When a belt rotating at a frequency fb' is connected to the rotating shaft of the electric motor 30, which rotates at a frequency fm', the vibration is transmitted to the rotor via the rotating shaft, and sidebands of f0±fb' appear in the frequency spectrum of the current detected by the current detector 41. Furthermore, harmonics such as f0±2fb', f0±3fb', and so on also appear in these sidebands.

[0100] These sidebands exhibit high signal intensities when the belt 61 is normally connected to the pulley Pu1, and decrease in signal intensity if damage such as breakage occurs. Since slip is also included in the characteristic frequency of the power transmission mechanism 60 as shown in equations (19) and (4), the flowcharts for calculating slip shown in Figures 8 and 9 of the first embodiment can also be applied to the power transmission mechanism 60. In this case, the flowchart for identifying the characteristic frequency shown in Figure 13 of the first embodiment becomes the flowchart shown in Figure 19 in the second embodiment.

[0101] As shown in Fig. 19, in step S601, the calculated value of slip is read. Next, in step S602, the characteristic frequency of the mechanical system abnormality is identified. Next, in step S603, the slip is corrected. Finally, in step S604, the characteristic frequency of the rotor bar damage is identified, and in step S605, the characteristic frequency of the power transmission mechanism is identified.

[0102] As described above, according to the second embodiment, similar to the first embodiment, it is possible to provide a diagnostic device for an electric motor that can diagnose abnormalities in a power transmission mechanism connected to an electric motor with high accuracy.

[0103] Embodiment 3. In the first and second embodiments, when comparing the noise frequency range with the characteristic frequency used to detect an abnormality in the electric motor or the power transmission mechanism of the electric motor, the noise frequency range is specified as a uniform value, for example, (+ / -) 1 Hz, for the noise frequency calculated by the above-mentioned equations (1), (2), and (3). In the third embodiment, the noise frequency range may be specified using the voltage frequency spectrum detected by the voltage detector 42. When an abnormality occurs in the electric motor 30 or the power transmission mechanism 60 of the electric motor, the signal strength of the characteristic frequency corresponding to each abnormality changes. This is because the impedance Z(ω) shown in the following equation (20) changes locally at each characteristic frequency.

[0104] I(ω)=V(ω) / Z(ω) (20)

[0105] Therefore, if noise occurs in the frequency spectrum of the voltage supplied to the electric motor 30, noise will also occur in the current frequency spectrum at the same frequency.

[0106] Figure 20 shows an example of the current frequency spectrum and voltage frequency spectrum obtained when a normal electric motor is operated. In Figure 20, the black solid line represents the voltage frequency spectrum waveform, and the gray solid line represents the current frequency spectrum waveform. Furthermore, MA1 represents the lower sideband of the characteristic frequency of the mechanical system anomaly, MA2 represents the upper sideband of the characteristic frequency of the mechanical system anomaly, RA1 represents the lower sideband of the rotor bar damage, and RA2 represents the upper sideband of the characteristic frequency of the rotor bar damage.

[0107] When the measurement conditions for the motor that produces the spectrum waveform shown in Figure 20 are applied to equations (1), (2), and (3), it is found that the calculated noise of the power converter occurs at 10 Hz intervals. This power converter noise can also be confirmed in the current frequency spectrum waveform and the voltage frequency spectrum waveform. It can also be confirmed that the waveform of the spectrum peak of the voltage frequency corresponding to the frequency of the power converter noise roughly matches the waveform of the spectrum peak of the current frequency. Therefore, it can be seen that using the voltage frequency spectrum is effective when determining whether the characteristic frequency and the power converter noise are superimposed.

[0108] 21 and 22 are flowcharts showing learning in the motor diagnostic device according to embodiment 3. Note that the following mainly describes the steps in embodiment 3 that involve the determination of the voltage frequency spectrum, and the same content as in embodiment 1 applies to the other steps.

[0109] First, in step S701, a user inputs specifications (data) of the electric motor 30 and the power conversion device 20 into the input unit 51A of the monitoring and diagnosing unit 51. Next, in step S702, the detection unit 51C detects the time waveforms of the current and voltage supplied from the power conversion device 20 to the electric motor 30 using the current detector 41 and the voltage detector 42, respectively. Next, in step S703, the analysis unit 51D performs spectrum analysis on the time waveform of the current detected by the current detector 41 and the time waveform of the voltage detected by the voltage detector 42, and calculates the frequency spectra of the current and the voltage, respectively. Next, in step S704, the analysis unit 51D extracts the drive frequency f0 with the highest signal strength from the calculated current frequency spectrum.

[0110] Next, in step S705, the analysis unit 51D extracts a voltage frequency spectrum, and in step S710, specifies a noise frequency range based on the voltage frequency spectrum. That is, for a voltage frequency spectrum such as that shown in FIG. 20, a floor level is defined as a predetermined signal strength, for example, -90 dB, and a frequency range having a signal strength equal to or greater than this is specified as the noise frequency range. Alternatively, the full width at half maximum may be calculated for the peak of the voltage frequency spectrum, and the full width at half maximum may be specified as the noise frequency range.

[0111] Next, in step S706, the calculation unit 51E calculates the slip of the electric motor 30 based on the specifications input by the input unit 51A, the current and voltage data input by the detection unit 51C, and the drive frequency acquired by the analysis unit 51D. The calculation of the slip of the electric motor 30 is the same as in the first embodiment, and therefore a detailed description thereof will be omitted.

[0112] Next, in step S707, the calculation unit 51E calculates the characteristic frequencies of the mechanical system abnormality, the rotor bar damage, and the power transmission mechanism abnormality from equations (4), (5), and (19) based on the slip s calculated in step S706, the drive frequency f0, and the number of pole pairs p of the electric motor 30.

[0113] Next, in step S708, the calculation unit 51E sends the characteristic frequencies of the mechanical system abnormality, rotor bar damage, and power transmission mechanism to the analysis unit 51D, and identifies each characteristic frequency from the current frequency spectrum analyzed by the analysis unit 51D.

[0114] Next, in step S711, the analysis unit 51D compares the feature frequency identified in step S708 with the noise frequency range specified based on the voltage frequency spectrum in step S705, and determines whether the identified feature frequency is included in the noise frequency range.

[0115] In step S711, if the characteristic frequencies identified in step S708 are not included in the range of noise frequencies specified in step S710, the process proceeds to step S712, where those characteristic frequencies and their signal intensities are learned as normal values.

[0116] In step S711, if all the characteristic frequencies identified in step S708 are included in the noise frequency range specified in step S710, the process proceeds to step S713, where it is determined that diagnosis is not possible.

[0117] When the learning period of the electric motor diagnostic device described with reference to Figures 21 and 22 ends, the drive frequency f0 of electric motor 30, the identified characteristic frequency, and the range of normal values ​​of the signal strength of the identified characteristic frequency are defined.

[0118] Thereafter, diagnosis by the electric motor diagnostic device is performed as shown in the flowcharts of Figures 23 and 24. In the flowcharts of Figures 23 and 24, first, in step S801, learning data is read. Here, the drive frequency f0 of electric motor 30, the identified feature frequency, and the normal value range of the signal strength of the identified feature frequency learned in the learning flowcharts of Figures 21 and 22 are read. Then, the following steps are performed: a current and voltage detection step in step S802, an analysis step in step S803, a drive frequency extraction step in step S804, a voltage frequency spectrum extraction step in step S805, a slip calculation step in step S806, a feature frequency calculation step in step S807, a feature frequency identification step in step S808, a noise frequency range designation step in step S810, and a determination step of whether the feature frequency is included in the noise frequency range in step S811. These steps are similar to the current and voltage detection step S702, the analysis step S703, the drive frequency extraction step S704, the voltage frequency spectrum extraction step S705, the slip calculation step S706, the feature frequency calculation step S707, the feature frequency identification step S708, the noise frequency range designation step S710, and the determination step S711 of whether the feature frequency is included in the noise frequency range, which are performed in the flowcharts of FIGS. 21 and 22 .

[0119] If, in step S811, the feature frequency is within the noise frequency range, the process proceeds to step S815, where diagnosis is deemed impossible and a message indicating this is displayed on the display unit 52. If, in step S811, the feature frequency is not within the noise frequency range, the process proceeds to step S812, where it is determined whether the signal strength of the feature frequency is outside the normal range. If, in step S812, the signal strength of the feature frequency is not outside the normal range, the process returns to step S802, where the diagnosis continues. If, in step S812, the signal strength of the feature frequency is outside the normal range, the process proceeds to step S813, where the location of the abnormality is identified. Then, in step S814, an alarm and the identified location of the abnormality are displayed on the display unit 52.

[0120] As described above, according to the third embodiment, the analysis unit performs spectrum analysis based on the voltage input by the detection unit to extract a voltage frequency spectrum, the calculation unit specifies a noise frequency range by the power conversion device based on the voltage frequency spectrum extracted by the analysis unit, and the determination unit determines the abnormality based on the feature frequency that is not included in the noise frequency range specified by the calculation unit, so that a highly accurate motor diagnostic device that can effectively remove the noise frequencies can be provided.

[0121] Fourth Embodiment Fig. 25 is a block diagram showing a diagnostic system for an electric motor according to a fourth embodiment. In the first to third embodiments, the diagnostic device 50 is connected to the current detector 41 and the voltage detector 42, and the diagnostic device 50 is arranged near these detectors 41 and 42 to diagnose abnormalities in the electric motor 30. In this case, the processing capacity of the analysis unit 51D or the calculation unit 51E of the monitoring and diagnosing unit 51 shown in Fig. 10 or the storage capacity of the memory unit 51B is limited, which poses a problem of limiting the diagnostic capability of the diagnostic device 50.

[0122] To solve this problem, in the fourth embodiment, the measuring device 400 that measures the current and voltage is separated from the diagnostic device 500, and the values ​​measured by the measuring device 400 are transmitted to the diagnostic device 500 via the network 405. This makes it possible to increase the processing capacity of the analysis and calculation unit 504 of the diagnostic device 500 that diagnoses abnormalities in the electric motor 30, or the storage capacity of the second memory unit 502, thereby improving the diagnostic capability of the diagnostic device 500.

[0123] A diagnosis system for an electric motor according to a fourth embodiment will be described below with reference to Fig. 25. The diagnosis system according to the fourth embodiment includes a measuring device 400 and a diagnosis device 500. The measuring device 400 includes a current detector 41 that detects the current supplied from the power conversion device 20 to the electric motor 30, a voltage detector 42 that detects the voltage supplied from the power conversion device 20 to the electric motor 30, a detection unit 401 that receives as input the current data detected by the current detector 41 and the voltage data detected by the voltage detector 42, a first memory unit 402 that stores the current data and voltage data input to the detection unit 401, and a network output unit 403 that outputs the current data and voltage data stored in the first memory unit 402 to a network 405. The diagnostic device 500 includes a network input unit 501 that receives data output from the network output unit 403 via the network 405, an input unit 503 that receives specifications of the power conversion device 20 and the electric motor 30, a second memory unit 502 that stores the data received from the network input unit 501 and the data received from the input unit 503, an analysis operation unit 504 that performs a current spectrum analysis based on the data stored in the second memory unit 502 to obtain a current frequency spectrum and a drive frequency of the electric motor 30, calculates a slip s of the electric motor 30 based on the specifications received from the input unit 503, the current and voltage data detected by the detection unit 401, and the drive frequency, and identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of an abnormality in the electric motor 30 based on the calculated slip s, and a determination unit 505 that determines an abnormality in the electric motor 30 based on the signal strength of the characteristic frequency identified by the analysis operation unit 504.

[0124] The analysis and calculation unit 504 of the fourth embodiment has the combined functions of the analysis unit 51D and calculation unit 51E of the first, second, and third embodiments. Furthermore, if the diagnostic device 500 is a general-purpose personal computer, for example, the input unit 503 corresponds to a keyboard, the display unit 506 corresponds to a display, the network input unit 501 corresponds to a receiving antenna, the analysis and calculation unit 504 and the determination unit 505 correspond to a central processing unit, and the software that operates the analysis and calculation unit 504 and the determination unit 505 corresponds to FIGS. 11, 12, 16, and 17 of the first embodiment, or FIGS. 21, 22, 23, and 24 of the third embodiment. Alternatively, the diagnostic device 500 may be a general-purpose tablet or a dedicated device. This makes it possible to solve the problems of the processing power and storage capacity of the diagnostic device 50, which are constraints when implementing the processing of the first, second, and third embodiments.

[0125] Next, a description will be given of the diagnosis of an electric motor by a diagnostic device according to embodiment 4. During learning before diagnosing an abnormality in electric motor 30, diagnostic device 500 drives a normal electric motor 30, identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of an abnormality by calculating the slip of electric motor 30, and stores the identified characteristic frequency, the signal strength of the identified characteristic frequency, and the drive frequency of the electric motor acquired by the analysis and calculation unit as normal data. During diagnosis when diagnostic device 500 diagnoses an abnormality in electric motor 30, it drives electric motor 30, identifies a new characteristic frequency of the current frequency spectrum that serves as an indicator of an abnormality by calculating the slip of electric motor 30, and determines whether electric motor 30 has an abnormality by comparing the newly identified characteristic frequency, the signal strength of the newly identified characteristic frequency, and the drive frequency of electric motor 30 newly acquired by the analysis and calculation unit with the normal data.

[0126] Furthermore, the analysis calculation unit 504 specifies a noise frequency range by the power conversion device 20 based on the drive frequency and the specifications of the power conversion device 20 input by the input unit 503. The determination unit 505 determines whether there is an abnormality in the electric motor 30 based on a feature frequency that is not included in the noise frequency range specified by the analysis calculation unit 504.

[0127] Alternatively, the analysis calculation unit 504 performs spectrum analysis based on the voltage detected by the voltage detector 42 to extract a voltage frequency spectrum, and based on the extracted voltage frequency spectrum, specifies a noise frequency range by the power conversion device 20. The determination unit 505 determines whether there is an abnormality in the electric motor 30 based on a characteristic frequency that is not included in the noise frequency range specified by the analysis calculation unit 504.

[0128] Furthermore, according to the fourth embodiment, by dividing the functions of the diagnostic device 50 of the first, second, and third embodiments into a diagnostic device 500 and a measuring device 400, it is possible to store and analyze data acquired from a plurality of measuring devices 400A, 400B, and 400C in a single diagnostic device 500. Fig. 26 shows an example of a configuration in which data acquired from three measuring devices 400A to 400C is stored and analyzed in a single diagnostic device 500. The upper limit on the number of measuring devices 400 that a single diagnostic device 500 will receive data from is determined by the storage capacity of the second memory unit 502 of the diagnostic device 500 or the processing capabilities of the analysis and calculation unit 504 and the determination unit 505, the amount of data transmitted from each measuring device 400, and other factors.

[0129] As described above, according to the fourth embodiment, there is provided a diagnostic system for an electric motor that diagnoses an abnormality in at least one of an electric motor driven by power converted by a power conversion device and a power transmission mechanism connected to the electric motor, the diagnostic system comprising: a measuring device; and a diagnostic device, the measuring device comprising: a current detector that detects a current supplied from the power conversion device to the electric motor; a voltage detector that detects a voltage supplied from the power conversion device to the electric motor; a detecting unit that inputs current data detected by the current detector and voltage data detected by the voltage detector; a first memory unit that stores the current data and voltage data input to the detecting unit; and a network output unit that outputs the current data and voltage data stored in the first memory unit to a network, the diagnostic device comprising: a network input unit that inputs data output from the network output unit via the network; an input unit that inputs specifications of the power conversion device and the electric motor; and a second memory unit that stores data input from the network input unit and data input from the input unit. The diagnostic device includes an analysis and calculation unit that performs a current spectrum analysis based on the data stored in the second memory unit to obtain a current frequency spectrum and a drive frequency of the electric motor, calculates a slip of the electric motor based on the specifications input by the input unit, the current and voltage data detected by the detection unit, and the drive frequency, and identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality based on the calculated slip, and a determination unit that determines the abnormality based on the signal strength of the characteristic frequency identified by the analysis and calculation unit.As a result, the effects of the first to third embodiments can be achieved, and the processing capacity of the analysis and calculation unit of the diagnostic device that diagnoses abnormalities in the electric motor or the storage capacity of the second memory unit can be increased, thereby improving the diagnostic ability of the diagnostic device.

[0130] The memory unit 51B, analysis unit 51D, calculation unit 51E, and judgment unit 51F of the monitoring and diagnosis unit 51 in the first to third embodiments, and the second memory unit 502, analysis and calculation unit 504, and judgment unit 505 of the diagnosis device 500 in the fourth embodiment are configured with a processor 1000 and a storage device 1010, as shown in FIG. 27 , as an example of their hardware. The storage device 1010 includes a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory, both not shown. Alternatively, a hard disk auxiliary storage device may be provided instead of the flash memory. The processor 1000 executes a program input from the storage device 1010. 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 1010, or may store the data in the auxiliary storage device via the volatile storage device.

[0131] 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.

[0132] 10 AC power supply, 20 power conversion device, 22 control circuit unit, 23 setting unit, 30 electric motor, 41 current detector, 42 voltage detector, 50 diagnostic device, 51 monitoring and diagnosis unit, 51A input unit, 51B memory unit, 51C detection unit, 51D analysis unit, 51E calculation unit, 51F judgment unit, 52 display unit, 60 power transmission mechanism, 70 load, 400, 400A, 400B, 400C measuring device, 401 detection unit, 402 first memory unit, 403 network output unit, 500 diagnostic device, 501 network input unit, 502 second memory unit, 503 input unit, 504 analysis and calculation unit, 505 judgment unit, 506 display unit.

Claims

1. A diagnostic device for an electric motor that diagnoses an abnormality in at least one of the following: an electric motor driven by power converted by a power converter, and a power transmission mechanism connected to the electric motor; The diagnostic device is A current detector for detecting the current supplied from the power converter to the electric motor, A voltage detector for detecting the voltage supplied from the power converter to the electric motor, The system includes a monitoring and diagnostic unit that monitors the abnormality based on the current detected by the current detector and the voltage detected by the voltage detector, The aforementioned monitoring and diagnostic unit, An input unit for inputting the specifications of the power converter and the electric motor, A detection unit that receives the current detected by the current detector and the voltage detected by the voltage detector, An analysis unit performs spectral analysis based on the current input by the detection unit and obtains the current frequency spectrum and the drive frequency of the electric motor. A calculation unit calculates the slip of the motor based on the parameters input by the input unit, the current and voltage data input by the detection unit, and the drive frequency acquired by the analysis unit, and identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality based on the calculated slip, A diagnostic device for an electric motor, comprising: a determination unit that determines the abnormality based on the signal strength of a characteristic frequency identified by the calculation unit; and a determination unit that determines the abnormality based on the signal strength of the characteristic frequency identified by the calculation unit.

2. During the learning phase before the diagnostic device performs the diagnosis of the abnormality, it drives the motor in a normal state. The characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality is identified by calculating the slip of the motor, and the identified characteristic frequency, the signal intensity of the identified characteristic frequency, and the drive frequency of the motor obtained by the analysis unit are stored as normal data. When the diagnostic device performs a diagnosis of the abnormality, it drives the electric motor. The motor diagnostic device according to claim 1, which newly identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality by calculating the slip of the motor, and determines the abnormality by comparing the newly identified characteristic frequency, the signal intensity of the newly identified characteristic frequency, and the drive frequency of the motor newly acquired by the analysis unit with the normal data.

3. The calculation unit specifies the noise frequency range for the power converter based on the drive frequency obtained by the analysis unit and the specifications of the power converter input by the input unit. The diagnostic device for an electric motor according to claim 1, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the calculation unit.

4. The calculation unit specifies the noise frequency range for the power converter based on the drive frequency obtained by the analysis unit and the specifications of the power converter input by the input unit. The diagnostic device for an electric motor according to claim 2, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the calculation unit.

5. The analysis unit performs spectral analysis based on the voltage input by the detection unit and extracts a voltage frequency spectrum. The calculation unit, based on the voltage frequency spectrum extracted by the analysis unit, specifies the noise frequency range of the power converter. The diagnostic device for an electric motor according to claim 1, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the calculation unit.

6. The analysis unit performs spectral analysis based on the voltage input by the detection unit and extracts a voltage frequency spectrum. The calculation unit, based on the voltage frequency spectrum extracted by the analysis unit, specifies the noise frequency range of the power converter. The diagnostic device for an electric motor according to claim 2, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the calculation unit.

7. A diagnostic device for an electric motor according to any one of claims 1 to 6, wherein the characteristic frequency identified based on the slip calculated in the calculation unit is corrected based on the waveform of the current frequency spectrum acquired in the analysis unit.

8. A diagnostic system for an electric motor that diagnoses an abnormality in at least one of the following: an electric motor driven by power converted by a power converter, and a power transmission mechanism connected to the electric motor; The diagnostic system comprises a measuring device and a diagnostic device, The measuring device is, A current detector for detecting the current supplied from the power converter to the electric motor, A voltage detector for detecting the voltage supplied from the power converter to the electric motor, A detection unit that inputs current data detected by the current detector and voltage data detected by the voltage detector, A first memory unit that stores current data and voltage data input to the detection unit, The system includes a network output unit that outputs current data and voltage data stored in the first memory unit to the network. The diagnostic device is A network input unit that receives data output from the network output unit via the network, An input unit for inputting the specifications of the power converter and the electric motor, A second memory unit that stores data input from the network input unit and data input from the input unit, An analysis calculation unit that, based on the data stored in the second memory unit, performs current spectrum analysis to obtain the current frequency spectrum and the drive frequency of the motor, calculates the slip of the motor based on the parameters input by the input unit, the current and voltage data detected by the detection unit, and the drive frequency, and identifies characteristic frequencies of the current frequency spectrum that serve as indicators of the abnormality based on the calculated slip, A diagnostic system for an electric motor, comprising: a determination unit that determines the abnormality based on the signal intensity of a characteristic frequency identified by the analysis calculation unit.

9. During the learning phase before the diagnostic device performs the diagnosis of the abnormality, it drives the motor in a normal state. The characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality is identified by calculating the slip of the motor, and the identified characteristic frequency, the signal intensity of the identified characteristic frequency, and the drive frequency of the motor obtained by the analysis calculation unit are stored as normal data. When the diagnostic device performs a diagnosis of the abnormality, it drives the electric motor. The motor diagnostic system according to claim 8, which newly identifies a characteristic frequency of the current frequency spectrum that serves as an indicator of the abnormality by calculating the slip of the motor, and determines the abnormality by comparing the newly identified characteristic frequency, the signal intensity of the newly identified characteristic frequency, and the drive frequency of the motor newly acquired by the analysis calculation unit with the normal data.

10. The analysis calculation unit specifies the noise frequency range of the power converter based on the drive frequency and the specifications of the power converter input by the input unit. The electric motor diagnostic system according to claim 8, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the analysis calculation unit.

11. The analysis calculation unit specifies the noise frequency range of the power converter based on the drive frequency and the specifications of the power converter input by the input unit, The electric motor diagnostic system according to claim 9, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the analysis calculation unit.

12. The analysis calculation unit performs spectral analysis based on the voltage detected by the voltage detector to extract a voltage frequency spectrum, and based on the extracted voltage frequency spectrum, specifies the noise frequency range of the power converter. The electric motor diagnostic system according to claim 8, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the analysis calculation unit.

13. The analysis calculation unit performs spectral analysis based on the voltage detected by the voltage detector to extract a voltage frequency spectrum, and based on the extracted voltage frequency spectrum, specifies the noise frequency range of the power converter. The electric motor diagnostic system according to claim 9, wherein the determination unit determines the abnormality based on the characteristic frequency that is not included in the noise frequency range specified by the analysis calculation unit.

14. A motor diagnostic system according to any one of claims 8 to 13, wherein the characteristic frequency identified based on the slip calculated in the analysis calculation unit is corrected based on the waveform of the current frequency spectrum acquired by the analysis calculation unit.