Electric motor diagnostic device and electric motor diagnostic system

Abstract

The present invention is provided with: a storage unit (51E) that stores a monitorable frequency band [F], and a monitoring feature frequency sequence [G] and the signal strength thereof; and a determination unit [51F] that determines at least one of an abnormality of an electric motor (3) and an abnormality of a power transmission mechanism (60) by comparing the monitorable frequency band [F] and monitoring feature frequency sequence [G] stored in the storage unit (51E) with a current spectrum newly acquired from a current detector (4).

Description

[Technical Field]

[0001] The present application relates to a motor diagnostic device and a motor diagnostic system. [Background Art]

[0002] When diagnosing an abnormality of a motor driven by pulse width modulation control of a power converter from current, a large number of spectral peaks derived from the power converter exist in the frequency spectral band used for abnormality diagnosis, compared to the case where the motor is driven by a commercial power supply. When the frequencies of these spectral peaks overlap with the characteristic frequencies for capturing an abnormality of the motor, this leads to false detection by the motor diagnostic device.

[0003] To address this problem, for example, the following Patent Document 1 theoretically calculates noise predicted to be generated from specification information of a motor and a power converter, and avoids false detection by comparing the noise with spectral peaks extracted during abnormality diagnosis.

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

[0005] [Patent Document 1] Japanese Patent No. 6824494 [Summary of the Invention] [Problem to be Solved by the Invention]

[0006] Conventional technology theoretically calculates noise frequencies using the modulation frequency, carrier frequency, sampling frequency, and power supply frequency in the frequency setting unit, and excludes these frequencies from the frequencies used for anomaly diagnosis. However, in actual measurements, there is a problem in that unpredictable noise appears in addition to these theoretically calculated values.

[0007] This application discloses technology to solve the above-mentioned problems, and aims to provide a motor diagnostic device and motor diagnostic system that can diagnose motor abnormalities with high accuracy even when theoretically unpredictable noise is present. [Means for solving the problem]

[0008] The motor diagnostic device disclosed herein is A motor diagnostic device for diagnosing at least one of the following: an abnormality in a motor driven by power converted by a power converter, and an abnormality in a power transmission mechanism connected to the motor, The diagnostic device is A current detector for detecting the current flowing from the power converter to the electric motor, The system includes a monitoring and diagnostic unit that performs spectral analysis based on the current detected by the current detector and monitors for at least one abnormality in the electric motor and an abnormality in the power transmission mechanism. The aforementioned monitoring and diagnostic unit, at least (a) the modulation frequency f0 of the power converter, (b) the number of pole pairs p of the electric motor, (c) the rated rotational speed or rotational speed Nr as spec information for calculating the slip s of the electric motor, (c) if the power transmission mechanism exists, the drive pulley radius Dr and the belt length L. An input section for entering the specifications information, Based on the information from the input unit, the abnormal mode of the electric motor Mechanical system malfunctions and rotor bar damage, and Frequency components appearing in the current spectrum due to at least one abnormality in the power transmission mechanism of, Regarding the aforementioned mechanical system anomaly, the sideband fm' of the modulated wave frequency f0 (fm'=((1-s) / p)f0), Regarding the rotor bar damage, the sideband fr' (fr'=2·s·f0) of the modulated wave frequency f0, Regarding the abnormality in the power transmission mechanism, the sideband fb' of the modulated wave frequency f0 (fb' = ((2·π·Dr) / L)·fr: fr is the rotational frequency of the motor calculated from the rotational speed Nr), asA setting unit calculates a characteristic frequency sequence by performing calculations, and calculates a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specifications information of the power converter, An analysis unit that performs spectral analysis based on the current detected by the current detector to obtain a current spectrum, extracts a sequence of peak frequencies having spectral peaks with a preset signal intensity or higher in the monitoring frequency range from the current spectrum, compares the peak frequency sequence with the feature frequency sequence and the noise frequency sequence to derive the frequencies included in the noise frequency sequence and the frequencies not included in the feature frequency sequence from the peak frequency sequence as a sequence of frequencies to be excluded from determination, derives the frequencies included in the feature frequency sequence from the peak frequency sequence as a sequence of monitored feature frequencies, and derives a monitorable frequency band by subtracting the sequence of frequencies to be excluded from determination from the monitoring frequency range. A storage unit that stores the aforementioned monitorable frequency band, the aforementioned monitoring characteristic frequency sequence, and its signal strength, The system includes a determination unit that determines at least one of the following abnormalities: an abnormality in the electric motor or an abnormality in the power transmission mechanism, by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the memory unit with the current spectrum newly acquired from the current detector. The motor diagnostic system disclosed herein is The aforementioned electric motor is equipped with multiple diagnostic devices, The database device to which each of the aforementioned electric motors' diagnostic devices is connected, It is connected to the database device and the diagnostic device for each of the electric motors, and includes a diagnostic algorithm modification device for modifying the diagnostic algorithm of the diagnostic device for each of the electric motors. [Effects of the Invention]

[0009] According to the motor diagnostic device and motor diagnostic system disclosed herein, it is possible to diagnose motor abnormalities with high accuracy even when theoretically unpredictable noise is present. [Brief explanation of the drawing]

[0010] [Figure 1] It is a block configuration diagram showing the schematic configuration of the power conversion device and the motor diagnostic device according to the first embodiment. [Figure 2] It is a circuit configuration diagram showing the main circuit unit of the power conversion device according to the first embodiment. [Figure 3] It is a diagram for explaining an outline of the operation of the control circuit unit of the power conversion device according to the first embodiment. [Figure 4] It is a diagram showing a block configuration diagram of a monitoring and diagnosis unit of the motor diagnostic device according to the first embodiment. [Figure 5] It is a diagram showing a learning flow of the motor diagnostic device according to the first embodiment. [Figure 6] It is a diagram showing a learning flow of the motor diagnostic device according to the first embodiment. [Figure 7] FIG. 7A is a diagram showing an example of a frequency spectrum obtained when the motor according to the first embodiment is driven. FIG. 7B is a diagram schematically showing a monitorable frequency band [F] obtained by subtracting a determination exclusion frequency sequence [E] from a monitoring frequency range [B]. [Figure 8] It is a diagram showing a monitoring and diagnosis flow of the motor diagnostic device according to the first embodiment. [Figure 9] It is a diagram showing a monitoring and diagnosis flow of the motor diagnostic device according to the first embodiment. [Figure 10] It is a block diagram showing the schematic configuration of the power conversion device and the motor diagnostic device according to the first embodiment. [Figure 11] It is a diagram showing a block configuration diagram of a monitoring and diagnosis unit of the motor diagnostic device according to the second embodiment. [Figure 12] It is a diagram showing a learning flow of the motor diagnostic device according to the second embodiment. [Figure 13] It is a diagram showing a learning flow of the motor diagnostic device according to the second embodiment. [Figure 14]Figure 14A is a diagram showing an example of a frequency spectrum obtained when the motor according to Embodiment 1 is driven. Figure 14B is a diagram schematically showing the monitorable frequency band [F1] obtained by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1]. [Figure 15] This diagram shows the monitoring and diagnostic flow of the motor diagnostic device according to Embodiment 2. [Figure 16] This diagram shows the monitoring and diagnostic flow of the motor diagnostic device according to Embodiment 2. [Figure 17] This figure shows the confirmation flow of the monitoring feature frequency sequence [G] according to Embodiment 3. [Figure 18] This figure shows the confirmation flow of the monitoring feature frequency sequence [G] according to Embodiment 3. [Figure 19] This diagram shows a flow chart for proposing changes to the setting conditions of the power converter according to Embodiment 4. [Figure 20] This diagram shows a flow chart for proposing changes to the setting conditions of the power converter according to Embodiment 4. [Figure 21] This diagram shows a flow chart for proposing changes to the setting conditions of the power converter according to Embodiment 4. [Figure 22] Figure 22A is a diagram showing an example of a frequency spectrum obtained when the motor was driven before changing the setting conditions of the power converter according to Embodiment 4. Figure 22B is a diagram schematically showing the monitorable frequency band [F1] obtained by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1]. [Figure 23] Figure 23A is a diagram showing an example of a frequency spectrum obtained when the motor was driven after changing the setting conditions of the power converter according to Embodiment 4. Figure 23B is a diagram schematically showing the monitorable frequency band [F1] obtained by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1]. [Figure 24] This figure shows a block diagram of the monitoring and diagnostic unit of the motor diagnostic device according to Embodiment 5. [Figure 25] This figure shows a block diagram illustrating the motor diagnostic system according to Embodiment 5. [Figure 26] This figure shows examples of the hardware configuration of the monitoring and diagnostic unit of the electric motor diagnostic device in each embodiment. [Modes for carrying out the invention]

[0011] Embodiment 1. Figure 1 is a block diagram showing the schematic configuration of a power conversion device and an electric motor diagnostic device according to Embodiment 1. As shown in Figure 1, the power converter 20 converts the frequency of AC power from an AC power source 1, such as a commercial power source, and supplies that power to the motor 3. The diagnostic device 50 detects the current of at least one phase of the current supplied from the power converter 20 to the motor 3 using the current detector 4, and detects an abnormality in the motor 3 by analyzing the detected current. The current detector 4 may be built into the power converter 20 or it may be external.

[0012] The power converter 20 includes a main circuit unit 21 for converting the frequency of power, a control circuit unit 22 for operating the main circuit unit 21, and a power converter setting unit 23 for determining the settings of the control circuit unit 22.

[0013] The diagnostic device 50 includes a monitoring and diagnostic unit 51 that monitors and diagnoses abnormalities in the electric motor 3 from the current detected by the current detector 4, a display unit 52 that displays the results obtained by the monitoring and diagnostic unit 51, and an alarm unit 53 that emits an alarm when the monitoring and diagnostic unit 51 detects an abnormality. Network output units may be provided for the display unit 52 and the alarm unit 53 to remotely present this information to the user.

[0014] Figure 2 is a circuit diagram showing the main circuit section of the power converter according to Embodiment 1. As shown in Figure 2, 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 1 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 motor 3.

[0015] Converter 21A is composed of a three-phase bridge circuit having six diodes Da, with the input lines of each phase connected to the AC power supply 1. Inverter 21C is composed of a three-phase bridge circuit with six switching elements Q, each with diodes Db connected in antiparallel, with the output lines of each phase connected to the motor 3. The switching elements Q can be, for example, IGBTs (Insulated Gate Bipolar Transistors) or MOSFETs (metal-oxide-semiconductor field effect transistors). The switching operation of the inverter 21C is controlled by signals generated by the control circuit unit 22. The operation of the control circuit unit 22 follows the operating conditions of the power converter 20 set by the user in the power converter setting unit 23.

[0016] Note that the configuration of the converter 21A and inverter 21C is not limited to that shown in the illustration. Also, although the main circuit section 21 of the power conversion device 20 is shown to be connected to the AC power supply 1 and includes a converter 21A, it is sufficient to have an inverter 21C that converts DC power to AC power and supplies power to the motor 3, and the converter 21A is not required. Furthermore, in this example, the AC power supply 1, power converter 20, and motor 3 are shown as having a three-phase configuration, for example, but are not limited to this.

[0017] Figure 3 is a diagram illustrating the general operation of the control circuit section of the power conversion device according to Embodiment 1. The control circuit section 22 of the power converter 20 drives the main circuit section 21 of the power converter 20 using a pulse width modulation method. The pulse width modulation method modulates the frequency of the power supplied to the motor 3 by extracting short-time intervals from the DC voltage of the smoothing capacitor 21B. Short-time interval extraction is achieved by rapidly controlling the ON and OFF states of the switching element Q of the inverter 21C. The control signal G that controls the switching element Q is generated by the modulation wave generation section 22A, the signal discretization section 22B, and the signal extraction section 22C of the control circuit section 22. The modulated wave generation unit 22A oscillates a sine wave at the modulated wave frequency f0 input by the user. The signal discretization unit 22B samples the modulated wave output by the modulated wave generation unit 22A at frequency fs and obtains discrete data. The control circuit unit 22 compares the magnitude of the discrete data with the carrier frequency fc (e.g., a triangular wave) generated by the signal extraction unit 22C and generates a control signal G.

[0018] In the control signal generation process shown in Figure 3, noise is generated at frequencies that are integer multiples of the greatest common divisor of the modulation wave frequency f0, sampling frequency fs, and carrier frequency fc, i.e., GCD(f0, fs, fc). This noise is defined as Fpwm as shown in equation (1).

[0019] Fpwm = i·GCD(f0, fs, fc) (1)

[0020] However, i is a positive integer. For example, if f0 = 60 Hz, fs = 4000 Hz, and fc = 2000 Hz, then GCD(f0, fs, fc) = 20 Hz, and Fpwm will be 20 Hz, 40 Hz, 60 Hz, ... Since such low-frequency noise overlaps with the frequency spectrum range monitored by the diagnostic device 50, it needs to be processed appropriately.

[0021] In addition to the Fpwm mentioned above, there is also noise that overlaps the frequency band monitored by the diagnostic device 50, which is generated during the process in which the converter 21A of the power converter 20 generates a DC voltage. This noise is defined as Fv as shown in equation (2).

[0022] Fv = |m·fac ± n·f0| (2)

[0023] However, fac is the frequency of the power supplied to converter 21A, and m and n are positive integers. For example, if f0 = 60 Hz and fac = 50 Hz, then fv will be 10 Hz, 20 Hz, 30 Hz, ... Such low-frequency noise overlaps with the frequency spectrum range monitored by the diagnostic device 50 and therefore needs to be handled appropriately.

[0024] Furthermore, components that are integer multiples of the modulated wave are generated as harmonics of the modulated wave. These are defined as F0, as shown in equation (3).

[0025] F0 = k·f0 (3) However, k is a positive integer.

[0026] Other types of noise generated by the power converter 20 include those caused by the dead time set up to protect the switching elements from damage when generating the control signal G, those caused by overmodulation due to the conditions of the amplitude of the modulated wave and the carrier wave, and noise generated when extracting the control signal G. If these types of noise overlap with the frequency band monitored by the diagnostic device 50, they must be processed appropriately.

[0027] The current detector 4 detects the current generated by the power converter 20. The detected current is processed by the diagnostic device 50. First, the diagnostic device 50 learns the state in which the motor 3 is operating normally. That is, it performs spectral analysis on the current when the motor 3 is operating normally and stores its characteristics. When performing a diagnosis, it compares the current with the characteristics of the learned current.

[0028] If an abnormality occurs in motor 3, an anomaly will appear in the frequency corresponding to each abnormality mode. Here, we will describe mechanical system abnormalities and rotor bar damage in motor 3 as examples.

[0029] [Mechanical system abnormality] When a malfunction occurs in the mechanical system of motor 3, rotor vibration and eccentricity occur. This eccentricity causes the gap length between the rotor and stator to fluctuate periodically. This periodic fluctuation in gap length changes the electrical properties of motor 3, causing a slight disturbance in the current detected by the current detector 4. By spectrally analyzing this disturbance, the sidebands f0±fm' of the modulated wave frequency f0 can be identified. However, fm' is given by equation (4).

[0030] fm'=((1-s) / p)f0 (4) However, p and s are the pole logarithm and slip, respectively.

[0031] [Rotor bar damage] When damage occurs to the rotor bar of motor 3, an out-of-phase component (-s·f0) is generated in the current within the rotor bar. This out-of-phase component returns a current with a frequency of (1-s)·f0 into the stator. This current generates a torque oscillation with frequency 2s·f0, inducing a magnetic flux oscillation of (1±2·s)·f0. As a result, a slight disturbance occurs in the current detected by the current detector 4. Spectral analysis of this disturbance reveals the sidebands f0±fr' of the modulated wave frequency f0. However, fr' is given by equation (5).

[0032] fr'=2·s·f0 (5)

[0033] The slip s is defined by equation (6), where N0 is the rotational speed of the rotating magnetic field and Nr is the rotational speed of the rotor. s = (N0 - Nr) / N0 (6)

[0034] Furthermore, the rotor rotation speed Nr can be roughly estimated from the rated rotational speed of the motor 3, and 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 modulation wave frequency f0, so the slip s of the motor 3 can be roughly estimated.

[0035] N0 = (60 / p)f0 (7)

[0036] The sidebands of mechanical system abnormalities and rotor bar damage, represented by equations (4) and (5), are present on the frequency spectrum detected by the current detector 4 with a certain signal intensity even when the motor 3 is functioning normally. However, when an abnormality occurs, these signal intensities increase. The diagnostic device 50 diagnoses an abnormality in the motor 3 based on this increase in signal intensity.

[0037] Figure 4 is a block diagram showing the monitoring and diagnostic section of the motor diagnostic device according to Embodiment 1, and Figures 5 and 6 are diagrams showing the learning flow of the motor diagnostic device according to Embodiment 1. As shown in Figure 4, the monitoring and diagnostic unit 51 of the diagnostic device 50 includes a detection unit 51A, an analysis unit 51B, an input unit 51C, a setting unit 51D, a storage unit 51E, and a determination unit 51F. The functions of each unit during learning will be explained based on the learning flow shown in Figures 5 and 6.

[0038] As shown in Figures 5 and 6, the diagnostic device 50 performs the following steps to diagnose an abnormality in the electric motor 3. First, in step S10, the specifications of the target electric motor 3 and power converter 20 (for example, the number of pole pairs of the electric motor 3, the rated rotational speed, the input power frequency fac of the power converter 20, the modulation frequency f0, the carrier frequency fc, the sampling frequency fs, etc.) are input to the input unit 51C. This specifications information can be input directly to the input unit 51C of the diagnostic device 50, or it can be input remotely to the input unit 51C via a network.

[0039] Next, based on the input specifications, the setting unit 51D calculates a characteristic frequency sequence [A] used to diagnose abnormalities in the electric motor 3 in step S11, based on equations (4) and (5) described above.

[0040] Furthermore, in step S12, the setting unit 51D calculates a monitoring frequency range [B] to be used to monitor the aforementioned feature frequency sequence [A]. The monitoring frequency range [B] is determined by equations (4) and (5) described above. For example, consider monitoring an electric motor 3 with a slip s of 2% to 10% when the modulated wave frequency f0 = 40 Hz and the number of pole pairs p = 1, as shown in Figures 7A and 7B below. In this case, f0 + fm = 76 Hz to 79.2 Hz, f0 - fm = 0.8 Hz to 4 Hz, f0 + fr = 41.6 Hz to 48 Hz, and f0 - fr = 32 Hz to 38.4 Hz. The monitoring frequency range [B] is determined so that these frequencies can be monitored. Information regarding the slip s range in this case may be obtained or estimated from the datasheet of the electric motor 3, or it may be specified as a specification when the diagnostic device 50 is commercialized.

[0041] Next, in step S13, the setting unit 51D calculates a calculable sequence of noise frequencies [C] that is thought to be possible to occur within the monitoring frequency range [B] based on equations (1) to (3) described above. The setting unit 51D then sends the calculated feature frequency sequence [A], monitoring frequency range [B], and noise frequency sequence [C] to the analysis unit 51B.

[0042] Meanwhile, in step S14, the current detector 4 detects the current of the motor 3. The current of the motor 3 detected by the current detector 4 is then digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B. Next, in step S15, the analysis unit 51B performs spectral analysis on the current signal sent from the detection unit 51A using a current FFT (Fast Fourier Transform) or the like. Next, in step S16, the analysis unit 51B extracts the frequency and signal intensity of the spectral peaks. Then, in step S17, the analysis unit 51B stores a peak frequency sequence [D] of spectral peaks within the monitoring frequency range [B] in the storage unit 51E. Here, the peak frequency sequence [D] is a sequence of frequencies having spectral peaks with a preset signal intensity, for example, -65 dB or higher.

[0043] Next, in steps S18 to S22, the analysis unit 51B compares the peak frequency sequence [D] with the feature frequency sequence [A], the monitoring frequency range [B], and the calculable noise frequency sequence [C], which are frequencies sent from the setting unit 51D, and classifies the extracted peak frequency sequence [D] according to its cause. At this time, the calculable noise frequency sequence [C] and the unclassifiable frequency sequence, which are spectral peak frequencies that cannot be classified, are excluded from the monitoring frequency range [B] used by the diagnostic device 50 when performing abnormality diagnosis, thereby defining the monitorable frequency band [F] and storing it in the storage unit 51E. The width of the spectral peaks in the excluded judgment frequency sequence [E] may be a statistical width obtained during the learning period, or it may be a uniform width of, for example, several Hz. In this embodiment, for example, the case where the width is ±1.5 Hz is shown.

[0044] Steps S18 to S22 will be explained in detail below. In step S18, the analysis unit 51B determines whether the peak frequency sequence [D] is included in the noise frequency sequence [C] that can be calculated. If, in step S18, the peak frequency sequence [D] is included in the noise frequency sequence [C] that can be calculated, the process proceeds to step S20, and the sequence is stored in the storage unit 51E as the excluded frequency sequence [E]. In step S18, if the peak frequency sequence [D] is not included in the noise frequency sequence [C] that can be calculated, the process proceeds to step S19 to determine whether or not the peak frequency sequence [D] is included in the feature frequency sequence [A]. In step S19, if the peak frequency sequence [D] is not included in the feature frequency sequence [A], the process proceeds to step S20 to store it in the storage unit 51E as the excluded frequency sequence [E]. In step S20, once the list of excluded frequencies [E] is stored in the storage unit 51E, the process proceeds to step S21, where the monitorable frequency band [F] is obtained by subtracting the list of excluded frequencies [E] from the monitored frequency range [B] and stored in the storage unit 51E. On the other hand, if in step S19 the peak frequency sequence [D] is included in the feature frequency sequence [A], the process proceeds to step S22, and the sequence is stored in the storage unit 51E as the monitored feature frequency sequence [G].

[0045] Figure 7A shows an example of a frequency spectrum obtained when the motor according to Embodiment 1 is driven. Figure 7B is a schematic diagram showing the monitorable frequency band [F] obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B].

[0046] Figure 7A shows an example of the frequency spectrum obtained by the analysis unit 51B of the monitoring and diagnostic unit 51 when the motor 3 is driven under the conditions f0 (modulation frequency) = 40 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 2000 Hz, and fs (sampling frequency) = 4000 Hz. Let's say we specify a monitoring frequency range [B] for detecting anomalies as, for example, 0Hz to 100Hz. Furthermore, here we consider a frequency sequence with a predetermined signal intensity, for example, a spectral peak of -65 dB or higher, as the peak frequency sequence [C]. The Fpwm obtained by equation (1) is an integer multiple of 40 Hz and coincides with the F0 obtained by equation (3).

[0047] In Figure 7A, spectral peaks can be observed at 40 Hz and 80 Hz, respectively. The Fv values ​​obtained by equation (2) are 20Hz, 40Hz, 60Hz, 80Hz, and 100Hz, and the spectral peaks that can be confirmed from Figure 7A are also 20Hz, 40Hz, 60Hz, 80Hz, and 100Hz. Spectral peaks at frequencies predicted from these theoretical formulas that can also be confirmed from the measurement results are stored in the exclusion frequency sequence [E] as the calculable noise frequency sequence [C]. In addition to these, it can be confirmed that spectral peaks that cannot be explained by theoretical formulas such as equations (1) to (3) or the characteristic frequency sequence [A] occur, such as 10~18Hz and 21~30Hz. These spectral peaks are also stored in the exclusion frequency sequence [E] because if they overlap with the characteristic frequency sequence [A] when performing anomaly diagnosis, it may lead to false detection.

[0048] Figure 7B schematically shows the monitorable frequency band [F] obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B]. The diagnostic device 50 stores this monitorable frequency band [F] in the storage unit 51E during the learning period. In Figure 7B, the monitorable frequency band [F] represents the blank area obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B]. In the monitorable frequency band [F], for example, if a mechanical system abnormality and rotor bar damage occur in an electric motor 3 with 1 pole pair, the frequency ranges for diagnosing each abnormality are shown in Figure 7B by [P1] and [P2], and [Q1] and [Q2]. Specifically, [P1] and [P2] are the frequency ranges for diagnosing mechanical system abnormalities, and [Q1] and [Q2] are the frequency ranges for diagnosing rotor bar damage. Here, the slip range for mechanical system abnormalities is assumed to be 4-20%, and the slip range for rotor bar damage is assumed to be 2-12%. From the above, it can be seen that, under the usage conditions of the said scope, the method of this disclosure can detect mechanical abnormalities and rotor bar damage while avoiding the influence of noise.

[0049] Figures 8 and 9 show the monitoring and diagnostic flow of the motor diagnostic device according to Embodiment 1. In step S30, the determination unit 51F of the diagnostic device 50, having completed the aforementioned learning, reads the observable frequency band [F] and the monitoring feature frequency sequence [G] stored in the storage unit 51E. Then, as in the learning process, in step S31, the current of the motor 3 is detected by the current detector 4. The current of the motor 3 detected by the current detector 4 is then digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B. Next, in step S32, the analysis unit 51B performs spectral analysis on the current signal sent from the detection unit 51A using a current FFT (Fast Fourier Transform) or the like. Next, in step S33, the analysis unit 51B extracts the frequency and signal intensity of the spectral peaks and sends them to the determination unit 51F.

[0050] Next, in step S34, the determination unit 51F extracts spectral peaks from the analysis unit 51B that are within the monitorable frequency band [F]. Next, in step S35, it is determined whether the signal intensity of the spectral peaks within the monitorable frequency band [F] has changed beyond a preset intensity range compared to the spectral peaks during learning. If it is determined in step S35 that the signal intensity has changed beyond the aforementioned intensity range, the process proceeds to step S36, where it is determined whether the frequency of the spectral peak whose signal intensity has changed beyond the aforementioned intensity range matches the monitored feature frequency sequence [G]. On the other hand, if it is determined in step S35 that the intensity did not change beyond the aforementioned range, the process returns to step S31 for the next abnormality diagnosis, and the current of the motor 3 is detected by the current detector 4, or the abnormality diagnosis of the motor is terminated. If it is determined in step S35 that the intensity has not changed beyond the specified range, the determination result is stored in the storage unit 51E and the display unit 52 displays that it is normal.

[0051] In step S36, if it is determined that the frequency of the spectral peak matches the monitored feature frequency sequence [G], the process proceeds to step S37 to determine the location of the anomaly. Here, the frequency of the spectral peak is applied to equations (4) and (5) above to determine whether the cause of the anomaly is due to a mechanical system malfunction or rotor bar damage, and the process proceeds to step S38. In step S38, the result of the abnormality determination in step S37 is stored in the storage unit 51E, and is displayed on the display unit 52 of the diagnostic device 50, or an alarm is issued by the alarm unit 53.

[0052] If, in step S36, it is determined that the frequency of the spectral peak does not match the monitored characteristic frequency sequence [G], the process proceeds to step S38. The determination is made that the change in the signal intensity of the spectral peak is not due to a mechanical malfunction or rotor bar damage, but that something unusual is happening. This determination is stored in the storage unit 51E, and the result is displayed on the display unit 52 of the diagnostic device 50 or an alarm is issued by the alarm unit 53. Furthermore, the display unit 52 and alarm unit 53 of the diagnostic device 50 may be equipped with network output units, allowing this information to be presented to the user remotely.

[0053] As described above, according to Embodiment 1, A motor diagnostic device for diagnosing abnormalities in an electric motor driven by power converted by a power converter, The diagnostic device is A current detector for detecting the current flowing from the power converter to the electric motor, The system includes a monitoring and diagnostic unit that performs spectral analysis based on the current detected by the current detector and monitors for abnormalities in the electric motor, The aforementioned monitoring and diagnostic unit, At least an input unit for inputting specifications information of the power converter and the electric motor, A setting unit calculates a characteristic frequency sequence used to diagnose abnormalities in the electric motor, a monitoring frequency range used to monitor the abnormalities, and a noise frequency sequence that can be derived from the specifications of the power converter, based on the information from the input unit. An analysis unit that performs spectral analysis based on the current detected by the current detector to obtain a current spectrum, extracts a sequence of peak frequencies having spectral peaks with a preset signal intensity or higher in the monitoring frequency range from the current spectrum, derives a sequence of frequencies to be excluded from judgment and a sequence of monitoring characteristic frequencies by comparing the sequence of peak frequencies with the sequence of characteristic frequencies and the sequence of noise frequencies, and derives a monitorable frequency band by subtracting the sequence of frequencies to be excluded from judgment from the monitoring frequency range. A storage unit that stores the aforementioned monitorable frequency band, the aforementioned monitoring characteristic frequency sequence, and its signal strength, The system is equipped with a determination unit that determines an abnormality in the electric motor by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the memory unit with the current spectrum newly acquired from the current detector.

[0054] Furthermore, before the diagnostic device diagnoses an abnormality in the electric motor, The analysis unit stores the monitorable frequency band as normal data, the characteristic frequency sequence and its signal intensity in the storage unit. When the diagnostic device diagnoses an abnormality in the electric motor, The determination unit reads the monitorable frequency band and the feature frequency sequence and its intensity as normal data from the storage unit and compares it with the current spectrum newly acquired from the current detector. An abnormality is determined when the signal intensity of the monitoring feature frequency sequence included in the monitoring frequency band changes beyond a preset threshold compared to normal data.

[0055] Furthermore, when the diagnostic device diagnoses an abnormality in the electric motor, The input unit receives the specifications information of the power converter and the electric motor, The setting unit calculates the noise frequency sequence using the specifications information of the power converter input to the input unit, and calculates the characteristic frequency sequence using the specifications information of the power converter and the electric motor input to the input unit. The analysis unit classifies each spectral peak in the monitoring frequency range from the current spectrum acquired by the current detector into the noise frequency sequence, the feature frequency sequence, and the unclassifiable sequence. The noise frequency sequence and the unclassifiable frequency sequence are defined as the frequency sequence to be excluded from determination. The monitoring frequency band is derived from the aforementioned list of frequencies excluded from the determination process.

[0056] In Embodiment 1, by configuring the system as described above, it is possible to diagnose motor abnormalities with high accuracy even when theoretically unpredictable noise is present.

[0057] Embodiment 2. Figure 10 is a block diagram showing the schematic configuration of the power conversion device and the motor diagnostic device according to Embodiment 2. As shown in Figure 10, the power converter 20 converts the frequency of AC power from the AC power source 1 and supplies it to the motor 3. The motor 3 is connected to the load 7 via the power transmission mechanism 60. The diagnostic device 50 detects the current of at least one phase of the current supplied from the power converter 20 to the motor 3 using the current detector 4, and detects abnormalities in the motor 3 and the power transmission mechanism 60 of the motor 3 by analyzing the detected current. In Embodiment 1, the diagnostic device 50 diagnoses mechanical abnormalities and rotor bar damage in the electric motor 3, whereas in Embodiment 2, it also diagnoses the power transmission mechanism 60 connected to the electric motor 3. Furthermore, the diagnosis of mechanical system abnormalities and rotor bar damage of the electric motor 3 described in Embodiment 1 and the diagnosis of the power transmission mechanism 60 of the electric motor 3 described in Embodiment 2 can be easily combined. The following explanation will focus on the differences from Embodiment 1, and similar points will be omitted from the explanation.

[0058] As shown in Figure 10, the electric motor 3 is connected to the load 7 via a power transmission mechanism 60. The power transmission mechanism 60, for example, uses a belt 61 to connect a pulley Pu1 attached to the rotating shaft of the electric motor 3 and a pulley Pu2 attached to the rotating shaft of the load 7, thereby transmitting power from the electric motor 3 to the load 7. In this case, if the radius of pulley Pu1 is Dr, the length of belt 61 is L, and the rotational speed of electric motor 3 is Nr, then the length of the belt rotated by pulley Pu1 per unit time is 2πDrNr. From the ratio of this length to the total length of the belt L, the rotational frequency of the belt fb' can be determined as follows.

[0059] fb'=((2·π·Dr) / L)·fr (8)

[0060] However, fr is the rotational frequency of electric motor 3. When a belt rotating at frequency fb' is connected to the rotating shaft of an electric motor 3 rotating at frequency fr, the vibrations are transmitted to the rotor through the rotating shaft of the electric motor 3 and appear as sidebands of f0±fb' in the frequency spectrum of the current detected by the current detector 4. In addition, harmonics such as f0±2fb', f0±3fb', etc. also appear in these sidebands.

[0061] These sidebands show high signal intensity when the belt 61 is properly connected to the pulley Pu1, and decrease in signal intensity if damage such as breakage occurs. This is the opposite of the case of mechanical system abnormalities and rotor bar damage described in Embodiment 1, so a partially different procedure is required when diagnosing the power transmission mechanism 60 compared to Embodiment 1.

[0062] Figure 11 is a block diagram showing the monitoring and diagnostic section of the motor diagnostic device according to Embodiment 2, and Figures 12 and 13 are diagrams showing the learning flow of the motor diagnostic device according to Embodiment 2. As shown in Figure 11, the monitoring and diagnostic unit 51 of the diagnostic device 50 includes a detection unit 51A, an analysis unit 51B, an input unit 51C, a setting unit 51D, a storage unit 51E, and a determination unit 51F. The functions of each unit during learning will be explained based on the learning flows in Figures 12 and 13.

[0063] Figures 12 and 13 show the learning flow of the motor diagnostic device according to Embodiment 2. First, in step S110, the specifications of the electric motor 3, the power converter 20, and the power transmission mechanism 60 are input to the input unit 51C. In Embodiment 1, the specifications of the electric motor 3 and the power converter 20 were input, but in Embodiment 2, the specifications of the power transmission mechanism 60 (for example, the radius Dr of the pulley Pu1, the length L of the belt 61, the rotational speed Nr of the electric motor 3, etc.) are added.

[0064] Next, based on the input specifications, the setting unit 51D calculates a characteristic frequency sequence [A1] in step S111, based on equations (4), (5), and (8) described above, which is used when diagnosing abnormalities in the electric motor 3 and the power transmission mechanism 60. Furthermore, in step S112, the setting unit 51D calculates the monitoring frequency range [B1] used to monitor the aforementioned characteristic frequency sequence [A1]. The monitoring frequency range [B1] is determined by equations (4), (5), and (8) described above. Furthermore, in step S113, the setting unit 51D calculates a calculable noise frequency sequence [C1] that is considered to be possible to occur within the monitoring frequency range [B1] based on the aforementioned equations (1) to (3). The setting unit 51D then sends the calculated feature frequency sequence [A1], monitoring frequency range [B1], and calculable noise frequency sequence [C1] to the analysis unit 51B.

[0065] Meanwhile, in step S114, the current detector 4 detects the current of the motor 3. The current of the motor 3 detected by the current detector 4 is then digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B. Next, in step S115, the analysis unit 51B performs spectral analysis on the current signal sent from the detection unit 51A using a current FFT (Fast Fourier Transform) or the like. Next, in step S116, the analysis unit 51B extracts the frequency and signal intensity of the spectral peaks. Then, in step S117, the analysis unit 51B stores the peak frequency sequence [D1] of spectral peaks within the monitoring frequency range [B1] in the storage unit 51E.

[0066] Next, in steps S118 to S122, the analysis unit 51B compares the peak frequency sequence [D1] with the feature frequency sequence [A1], the monitoring frequency range [B1], and the calculable noise frequency sequence [C1], which are frequencies sent from the setting unit 51D, and classifies the extracted peak frequency sequence [D1] according to its cause. At this time, the calculable noise frequency sequence [C1] and the unclassifiable frequency sequence, which are spectral peak frequencies that cannot be classified, are excluded from the monitoring frequency range [B1] used by the diagnostic device 50 when performing abnormality diagnosis, thereby defining the monitorable frequency band [F1] and storing it in the storage unit 51E. The width of the spectral peaks in the excluded judgment exclusion frequency sequence [E1] may be a statistical width obtained during the learning period, or it may be a uniform width of, for example, several Hz. In this embodiment, for example, the case where the width is ±1.5 Hz is shown.

[0067] Steps S118 to S122 will be explained in detail below. In step S118, the analysis unit 51B determines whether the peak frequency sequence [D1] is included in the noise frequency sequence [C1] that can be calculated. If, in step S118, the peak frequency sequence [D1] is included in the noise frequency sequence [C1] that can be calculated, the process proceeds to step S120, where it is stored in the storage unit 51E as the excluded frequency sequence [E1]. In step S118, if the peak frequency sequence [D1] is not included in the noise frequency sequence [C1] that can be calculated, the process proceeds to step S119 to determine whether or not the peak frequency sequence [D1] is included in the feature frequency sequence [A1]. In step S119, if the peak frequency sequence [D1] is not included in the feature frequency sequence [A1], the process proceeds to step S120 to store it in the storage unit 51E as the excluded frequency sequence [E1]. In step S120, when the list of excluded frequencies [E1] is stored in the storage unit 51E, the process proceeds to step S121, where the monitorable frequency band [F1] is obtained by subtracting the list of excluded frequencies [E1] from the monitored frequency range [B1], and this band is stored in the storage unit 51E. On the other hand, if in step S119 the peak frequency sequence [D1] is included in the feature frequency sequence [A1], the process proceeds to step S122, and the sequence is stored in the storage unit 51E as the monitored feature frequency sequence [G1].

[0068] Figure 14A shows an example of a frequency spectrum obtained when the motor according to Embodiment 2 is driven. Figure 14B schematically shows the monitorable frequency band [F1] obtained by subtracting the excluded frequency sequence [E1] from the monitored frequency range [B1]. In Figure 14B, the monitorable frequency band [F1] represents the blank region obtained by subtracting the excluded frequency sequence [E1] from the monitored frequency range [B1].

[0069] Figure 14A shows an example of a frequency spectrum acquired by the current detector 4 when the motor 3 is driven under the conditions f0 (modulation frequency) = 40 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 2000 Hz, and fs (sampling frequency) = 4000 Hz. The following describes the differences from Embodiment 1, and the same details as in Embodiment 1 (descriptions of Figures 7A and 7B) will be omitted as appropriate.

[0070] Suppose a monitoring frequency range [B1] for detecting abnormalities is specified as, for example, 0 to 100 Hz. For example, under the conditions described above, suppose the power transmission mechanism 60 rotates a belt 61 with a length of 1 m on a pulley Pu1 with a radius of 10 cm. If the number of pole pairs of the rotating electric motor 3 is 1 and the slip at that time is 5%, then the sidebands observed when diagnosing the power transmission mechanism 60 will be, in order from the low frequency side, 16.12 Hz, 63.88 Hz, 87.75 Hz, ...

[0071] Comparing the frequency spectrum shown in Figure 14A with the sidebands observed when diagnosing the power transmission mechanism 60, it can be seen that 87.75 Hz is within the monitorable frequency band [F1]. In other words, when the power transmission mechanism 60 is connected, the sidebands at 16.12 Hz and 63.88 Hz in the frequency spectrum shown in Figure 14A are undetectable due to the presence of a large amount of noise, but 87.75 Hz has no noise or low noise intensity, making it possible to diagnose the power transmission mechanism 60. Note that this frequency was determined assuming a slip of 5%, but the frequency range when the slip is 1 to 16% is the frequency range [R] shown in Figure 14B. That is, [R] shown in Figure 14B is the frequency range for diagnosing the power transmission mechanism. Therefore, it is possible to adequately monitor the peaks for diagnosing the power transmission mechanism even with load fluctuations. Furthermore, Figures 14A and 14B also show the frequency ranges [P1] and [P2] for diagnosing mechanical system abnormalities, and the frequency ranges [Q1] and [Q2] for diagnosing rotor bar damage, similar to Figures 7A and 7B.

[0072] Figures 15 and 16 show the monitoring and diagnostic flow of the motor diagnostic device according to Embodiment 2. In step S130, the determination unit 51F of the diagnostic device 50, having completed the aforementioned learning, reads the observable frequency band [F1] and the monitoring feature frequency sequence [G1] stored in the storage unit 51E. Then, as in the learning process, in step S131, the current of the motor 3 is detected by the current detector 4. The current of the motor 3 detected by the current detector 4 is then digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B. Next, in step S132, the analysis unit 51B performs spectral analysis on the current signal sent from the detection unit 51A using a current FFT (Fast Fourier Transform) or the like. Next, in step S133, the analysis unit 51B extracts the frequency and signal intensity of the spectral peaks and sends them to the determination unit 51F.

[0073] Next, in step S134, the determination unit 51F extracts spectral peaks from the analysis unit 51B that are within the monitorable frequency band [F1]. Next, in step S135, it is determined whether the signal intensity of the spectral peaks within the monitorable frequency band [F1] has changed beyond a preset intensity range compared to the spectral peaks during learning. If it is determined in step S135 that the signal intensity has changed beyond the aforementioned intensity range, the process proceeds to step S136, where it is determined whether the frequency of the spectral peak whose signal intensity has changed beyond the aforementioned intensity range matches the monitored feature frequency sequence [G1]. On the other hand, if it is determined in step S135 that the intensity did not change beyond the aforementioned range, the process returns to step S131 for the next abnormality diagnosis, and the current of the motor 3 is detected by the current detector 4, or the abnormality diagnosis of the motor is terminated. Then, if it is determined in step S135 that the intensity did not change beyond the aforementioned range, the determination result is stored in the storage unit 51E and the display unit 52 displays that it is normal.

[0074] In step S136, if it is determined that the frequency of the spectral peak matches the monitored feature frequency sequence [G1], the process proceeds to step S137 to determine the location of the anomaly. Here, the frequency of the spectral peak is applied to equations (4) and (5) above to determine whether the cause of the anomaly is due to a mechanical system malfunction or rotor bar damage. Furthermore, the frequency of the spectral peak is applied to equation (8) above to determine whether the cause of the anomaly is due to the power transmission mechanism. Then, the process proceeds to step S138. In step S138, the result of the abnormality determination in step S137 is stored in the storage unit 51E, and is displayed on the display unit 52 of the diagnostic device 50, or an alarm is issued by the alarm unit 53.

[0075] If, in step S136, it is determined that the frequency of the spectral peak does not match the monitored characteristic frequency sequence [G1], the process proceeds to step S138. The determination is made that the change in the signal intensity of the spectral peak is not due to a mechanical system malfunction, rotor bar damage, or power transmission system malfunction, but that something unusual is happening. The determination is stored in the storage unit 51E, and the result is displayed on the display unit 52 of the diagnostic device 50 or an alarm is issued by the alarm unit 53. Furthermore, the display unit 52 and alarm unit 53 of the diagnostic device 50 may be equipped with network output units, allowing this information to be presented to the user remotely.

[0076] As mentioned at the beginning, in diagnosing the power transmission mechanism 60, a decrease in the signal intensity of the characteristic frequency sequence [G1] is diagnosed as an abnormality, which differs from the diagnosis of mechanical system abnormalities and rotor bar damage described in Embodiment 1. Therefore, this embodiment and Embodiment 1 can be easily combined.

[0077] As described above, according to Embodiment 2, A motor diagnostic device for diagnosing abnormalities in an electric motor driven by power converted by a power converter, and abnormalities in a power transmission mechanism connected to the electric motor, The diagnostic device is A current detector for detecting the current flowing from the power converter to the electric motor, The system includes a monitoring and diagnostic unit that performs spectral analysis based on the current detected by the current detector to monitor for abnormalities in the electric motor and the power transmission mechanism. The aforementioned monitoring and diagnostic unit, At least an input unit for inputting specifications information of the power converter and the electric motor, A setting unit calculates a characteristic frequency sequence used to diagnose abnormalities in the electric motor and the power transmission mechanism, a monitoring frequency range used to monitor the abnormalities, and a noise frequency sequence that can be derived from the specifications of the power converter, based on the information from the input unit. An analysis unit that performs spectral analysis based on the current detected by the current detector to obtain a current spectrum, extracts a sequence of peak frequencies having spectral peaks with a preset signal intensity or higher in the monitoring frequency range from the current spectrum, derives a sequence of frequencies to be excluded from judgment and a sequence of monitoring characteristic frequencies by comparing the sequence of peak frequencies with the sequence of characteristic frequencies and the sequence of noise frequencies, and derives a monitorable frequency band by subtracting the sequence of frequencies to be excluded from judgment from the monitoring frequency range. A storage unit that stores the aforementioned monitorable frequency band, the aforementioned monitoring characteristic frequency sequence, and its signal strength, The system is equipped with a determination unit that determines abnormalities in the electric motor and the power transmission mechanism by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the memory unit with the current spectrum newly acquired from the current detector.

[0078] Furthermore, before the diagnostic device diagnoses any abnormalities in the electric motor and the power transmission mechanism, The analysis unit stores the monitorable frequency band as normal data, the characteristic frequency sequence and its signal intensity in the storage unit. When the diagnostic device diagnoses an abnormality in the electric motor and an abnormality in the power transmission mechanism, The determination unit reads the monitorable frequency band and the feature frequency sequence and its intensity as normal data from the storage unit and compares it with the current spectrum newly acquired from the current detector. An abnormality is determined when the signal intensity of the monitoring feature frequency sequence included in the monitoring frequency band changes beyond a preset threshold compared to normal data.

[0079] Furthermore, when the diagnostic device diagnoses an abnormality in the electric motor and an abnormality in the power transmission mechanism, The input unit receives the specifications information of the power converter, the electric motor, and the power transmission mechanism. The setting unit calculates the noise frequency using the specifications information of the power converter input to the input unit, and calculates the characteristic frequency sequence using the specifications information of the power converter, the motor, and the power transmission mechanism input to the input unit. The analysis unit classifies each spectral peak from the current spectrum acquired by the current detector into the noise frequency sequence, the feature frequency sequence, and the unclassifiable sequence. The noise frequency sequence and the unclassifiable frequency sequence are defined as the frequency sequence to be excluded from determination. The monitoring frequency band is calculated from the aforementioned list of frequencies excluded from the determination process.

[0080] In Embodiment 2, by configuring the system as described above, it is possible to diagnose abnormalities in the electric motor and power transmission mechanism with high accuracy, even when theoretically unpredictable noise is present.

[0081] Embodiment 3. In Embodiment 3, in order to more clearly distinguish between the judgment exclusion frequency sequence [E] and the monitoring feature frequency sequence [G] in Embodiment 1, and in order to more clearly distinguish between the judgment exclusion frequency sequence [E1] and the monitoring feature frequency sequence [G1] in Embodiment 2, the learning of the diagnostic device 50 is performed while changing the load factor of the electric motor 3. Here, the fact that the characteristic frequencies of mechanical system abnormalities, rotor bar damage, and power transmission mechanism shown in equations (4), (5), and (8) above depend on the slip s is utilized.

[0082] Figures 17 and 18 show the verification flow of the monitoring feature frequency sequence [G] according to Embodiment 3. Figures 17 and 18 illustrate the flow of learning the diagnostic device 50 while changing the load factor of the motor 3 in the second embodiment, in order to more clearly separate the judgment exclusion frequency sequence [E1] and the monitoring feature frequency sequence [G1]. Changing the load factor of the motor 3 means, for example, if the load connected to the motor 3 is a pump, opening and closing its valve will cause the load factor to fluctuate.

[0083] As shown in Figure 13 of Embodiment 2, the storage unit 51E stores a sequence of frequencies to be excluded from determination [E1] and a sequence of frequencies to be monitored [G1]. In step S200, the analysis unit 51B reads out the judgment exclusion frequency sequence [E1] and the monitoring feature frequency sequence [G1] stored in the storage unit 51E. Next, in step S201, the load factor connected to the motor 3 is changed, and in step S202, the current is detected by the current detector 4. Next, in step S203, the analysis unit 51B performs spectral analysis on the detected current signal using a current FFT (Fast Fourier Transform) or the like. Next, in step S204, the analysis unit 51B extracts the frequency and signal intensity of the spectral peaks.

[0084] Next, in step S205, the analysis unit 51B compares the spectral peaks obtained in step S204 with the monitored frequency sequence [G1]. Here, the monitored frequency sequence [G1] consists of feature frequencies calculated based on equations (4), (5), and (8). Therefore, a change in the load factor appears as slip, and each feature frequency shifts. If no shift in these feature frequencies is detected (Yes in step S206), then these are not feature frequencies, and in step S207, the frequencies that do not shift are added to the exclusion frequency sequence [E1] and removed from the monitored feature frequency sequence [G1]. By following the procedure described above, it is possible to more clearly separate the exclusion frequency sequence [E1] from the monitoring feature frequency sequence [G1].

[0085] As described above, according to Embodiment 3, The aforementioned analysis unit, When checking the settings of the monitoring feature frequency sequence and the judgment exclusion frequency sequence, The load factor of the electric motor is changed and the current spectrum is obtained using the current detector. It was confirmed that the peaks of the current spectrum corresponding to each component of the monitoring characteristic frequency sequence shift depending on the load factor of the motor. If no shift is detected, the component of the monitoring feature frequency sequence corresponding to the peak of the current spectrum for which no shift was detected is removed from the monitoring feature frequency sequence and added to the judgment exclusion frequency sequence. This makes it possible to more clearly separate the frequency sequence of monitored features from the frequency sequence of excluded features.

[0086] Embodiment 4. In Embodiment 1, the specifications of the electric motor 3 and the power converter 20 were input to the diagnostic device 50 to diagnose the electric motor 3. In Embodiment 2, the specifications of the electric motor 3, the power converter 20, and the power transmission mechanism 60 were input to the diagnostic device 50 to diagnose the electric motor 3 and the power transmission mechanism 60. In each embodiment, the system learns the calculable noise frequency sequence generated by the power converter 20, and during diagnosis, it avoids performing diagnosis using the calculable noise frequency sequence. On the other hand, the noise generation situation by the power converter 20 depends on the settings of the power converter 20. Therefore, if there is a large amount of noise generated, the range of the monitorable frequency band [F], [F1] may not be sufficiently secured, and abnormal diagnosis may not be possible. Embodiment 4 describes how to address this problem.

[0087] Figures 19, 20, and 21 show a flowchart illustrating a proposed change in the setting conditions of the power converter 20 according to Embodiment 4. Note that the flows in Figures 19, 20, and 21 are equivalent to the flows in Figures 12 and 13, which represent the learning process in Embodiment 2.

[0088] In the flowcharts of Figures 19, 20, and 21, steps S310 to S322 are the same as steps S110 to S122 in Figures 12 and 13, so their explanation is omitted. In the flow charts shown in Figures 19 to 21, the monitorable frequency band [F1] and the monitored characteristic frequency sequence [G1] are stored in the storage unit 51E in steps S321 and S322. In this case, if the monitorable frequency band [F1] is narrower than the monitored frequency range [B1], it becomes difficult to perform anomaly diagnosis.

[0089] Figure 22A shows the frequency spectrum of the current detected by the current detector 4 as an example, when f0 (modulation frequency) = 119 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 1000 Hz, and fs (sampling frequency) = 4000 Hz. However, the monitoring frequency range [B1] for detecting abnormalities is set to 0 Hz to 220 Hz. It can be confirmed that spectral peaks occur throughout the entire monitoring frequency range [B1]. Similar to the example of the embodiment described above, spectral peaks with a signal intensity of -65 dB or higher are set as the exclusion frequency sequence [E1], and their width is set to ±1.5 Hz. Note that these frequencies and widths may both be statistical values ​​obtained through learning.

[0090] Figure 22B shows the excluded frequency sequence [E1] and the monitorable frequency band [F1] in the case of Figure 22A. As mentioned above, in Figure 22B, the monitorable frequency band [F1] is the frequency band indicated by the blank space between the excluded frequency sequence [E1]. Figure 22B shows that the observable frequency band [F1] is narrower than the monitored frequency range [B1], and the ratio of the observable frequency band [F1] to the monitored frequency range [B1] was 29%. It is difficult to perform anomaly diagnosis in such a narrow range.

[0091] Therefore, in this embodiment, a predetermined ratio threshold is set for the ratio, and if the ratio is below this threshold, a change in the setting conditions for the power converter 20 is suggested. Let's assume the ratio threshold is set to, for example, 50%. Under the conditions shown in Figure 22A, the ratio of the monitorable frequency band [F1] to the monitored frequency range [B1] is 29%, which is below the ratio threshold of 50%. Therefore, the diagnostic device 50 suggests to the power converter 20 that it change its setting conditions. In other words, in step S330, it is determined whether the ratio of the monitorable frequency band [F1] to the monitored frequency range [B1] is less than or equal to a preset ratio threshold. In step S330, if the ratio of the monitorable frequency band [F1] to the monitored frequency range [B1] is less than or equal to a preset ratio threshold, the process proceeds to step S340, where the setting conditions of the power converter 20 are changed. Then, with the settings conditions of the power converter 20 changed, the processing steps from step S310 to step S330 are repeated. Furthermore, in step S330, if the ratio of the monitorable frequency band [F1] to the monitored frequency range [B1] is greater than a preset ratio threshold, the learning process is terminated.

[0092] When suggesting changes to the setting conditions of the power converter 20, the proposed changes should deviate as little as possible from the original setting conditions, so as not to deviate from the intended use of the motor 3. One example of this is Fpwm, expressed by equation (1) above. In the conditions shown in Figures 22A and 22B, with i as a positive integer, Fpwm = i. According to equation (1), Fpwm should be generated every 1 Hz, but no such spectral peak can be seen in Figure 22A. However, as GCD(f0, fc, fs) increases, Fpwm according to equation (1) is generated on the frequency spectrum, and the proportion of noise tends to decrease when viewed as a whole frequency spectrum. Therefore, the diagnostic device 50 proposes shifting f0 (modulation frequency) = 119 Hz by 1 Hz to 120 Hz. In this case, Fpwm = 40i, Fpwm is generated every 40 Hz, and the generation of noise is suppressed.

[0093] Figure 23A shows the frequency spectrum of the detected current when f0 (modulation frequency) = 120 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 1000 Hz, and fs = 4000 Hz. Figure 23B shows the excluded frequency sequence [E1] and the monitorable frequency band [F1] in that case. As described above, the observable frequency band [F1] was expanded relative to the monitoring frequency range [B1], and the ratio of the observable frequency band [F1] to the monitoring frequency range [B1] was evaluated to be 77%. Such a wide range makes diagnosis possible. Furthermore, since the percentage change in f0 (modulation frequency) is only about 1%, it does not deviate from the intended use of the motor 3.

[0094] As described above, according to Embodiment 4, The aforementioned analysis unit, If the ratio of the monitorable frequency band to the monitoring frequency range is less than or equal to a preset ratio threshold, the system will suggest changing the settings of the power converter's specifications. By widening the ratio of the monitorable frequency band to the monitored frequency range, it becomes possible to perform highly accurate abnormality diagnosis of electric motors and improve reliability.

[0095] Embodiment 5. In Embodiments 1 to 4, the motor diagnostic device 50 diagnoses abnormalities in the motor 3 driven by the power converter 20, or in the power transmission mechanism 60 connected to the motor 3. In doing so, it calculates the noise generated from the power converter 20 according to the aforementioned equations (1), (2), and (3), and learns the noise generation conditions for each piece of equipment. The generation of this noise may depend not only on the operating principle of the power converter 20, but also on the model number of the power converter 20 or the motor 3, or on the installation conditions of these pieces of equipment. Embodiment 5 will explain how to address these problems.

[0096] Figure 24 is a block diagram showing the monitoring and diagnostic section of the motor diagnostic device according to Embodiment 5, and Figure 25 is a block diagram showing the motor diagnostic system according to Embodiment 5. As shown in Figure 24, the monitoring and diagnostic unit 51 of the motor diagnostic device 50 according to Embodiment 5 includes the detection unit 51A, analysis unit 51B, input unit 51C, setting unit 51D, storage unit 51E, and determination unit 51F described in the above embodiment, as well as a network input / output unit 51G that performs data input and output with an external device via a network. As shown in Figure 25, the motor diagnostic system according to Embodiment 5 has a database device 100 that stores various data transmitted from multiple motor diagnostic devices 50 (diagnostic device A, diagnostic device B, diagnostic device C) via a network input / output unit 51G. The database device 100 is connected to a diagnostic algorithm modification device 110, which can access the data stored in the database device 100 via the network. Furthermore, the diagnostic algorithm modification device 110 can transmit modified diagnostic algorithms to the multiple motor diagnostic devices 50 (diagnostic device A, diagnostic device B, diagnostic device C) via the network.

[0097] As described in the above-mentioned embodiment, each motor diagnostic device 50 (diagnostic device A, diagnostic device B, diagnostic device C) obtains specification information of the power converter 20, motor 3, and power transmission mechanism 60 according to the learning flow shown in Figures 5 and 6, or Figures 12 and 13, for example. Based on this specification information, it calculates the exclusion frequency sequence [E] or [E1], the monitorable frequency band [F] or [F1], and the monitoring characteristic frequency sequence [G] or [G1]. Furthermore, each motor diagnostic device 50 (diagnostic device A, diagnostic device B, diagnostic device C) outputs these calculated results and the obtained specification information of the power converter 20, motor 3, or power transmission mechanism 60 to the database device 100 via the network input / output unit 51G. At that time, the output information may also include the peak of the current spectrum compared by the determination unit 51F, the determination result of the determination unit 51F, and the model numbers of the power converter 20 and motor 3.

[0098] The database device 100 is installed in a location where data can be input from multiple motor diagnostic devices 50, but it is preferable to have data input from as many motor diagnostic devices 50 as possible. In other words, while it is possible to install the database device 100 within one facility and collect data from motor diagnostic devices 50 within that facility, it is preferable to install the database device 100 outside of the facility and collect data from multiple motor diagnostic devices 50 in multiple facilities.

[0099] The data stored in the database device is analyzed by the manufacturer's diagnostic algorithm modification device 110 of the motor diagnostic device 50. The analysis by the diagnostic algorithm modification device 110 uses the model numbers or setting conditions of the power converter 20, motor 3, and power transmission mechanism 60 stored in the database device 100, the exclusion frequency sequence [E] or [E1] calculated by each diagnostic device 50, the monitorable frequency band [F] or [F1], the monitoring characteristic frequency sequence [G], [G1], the peaks of the current spectrum compared by the determination unit 51F, and the determination results of the determination unit 51F. The diagnostic algorithm of each motor diagnostic device 50 is modified based on this data. After modification by the diagnostic algorithm modification device 110, the network input / output unit 51G provided in the monitoring and diagnosis unit 51 of each diagnostic device 50 receives the modification data, and the diagnostic algorithm of each motor diagnostic device 50 is modified.

[0100] One example of the proposed modifications is the change in the setting conditions of the power converter 20, as described in Embodiment 4. As described in Embodiment 4, the characteristics of the noise generated by the power converter 20 change depending on the magnitude of the GCD(f0, fc, fs) expressed by equation (1), for example. While it is possible to adjust the generated noise to some extent by changing the magnitude of the GCD(f0, fc, fs), it is difficult to grasp the detailed noise generation situation that depends on the model number of the power converter 20 or the motor 3. In this regard, the diagnostic algorithm will be modified to utilize the data of the database device 100 when proposing changes to the setting conditions. Suppose a large amount of noise is generated when a particular power converter 20 and motor 3 are driven under specific setting conditions, making diagnosis difficult. In that case, in order to fine-tune the settings of the power converter 20, it is possible to search for setting values ​​that are thought to improve noise generation from similar conditions in the data of the database device 100 and propose them to the user as a diagnostic algorithm. Alternatively, the manufacturer can analyze the data of the database device 100 and introduce a newly created diagnostic algorithm to improve the noise generation situation to each diagnostic device 50. However, modifications to these diagnostic algorithms are limited by the specifications of the equipment installed in each diagnostic device 50, such as calculation speed or memory capacity.

[0101] Another modification concerns the results of the diagnostic device 50's diagnosis of the electric motor 3 or the power transmission mechanism 60, as shown in Figures 8 and 9, or Figures 15 and 16. As described in Embodiments 1 to 4, the motor diagnostic device 50 diagnoses abnormalities in the motor 3 or power transmission mechanism 60 while avoiding noise generated from the power converter 20. However, a considerable number of false detections occur during the diagnostic process. These false detections may be due, for example, to the setting method of the exclusion frequency sequences [E] and [E1] that the diagnostic device 50 learns. In Embodiments 1 and 2, the spectral peaks of the peak frequency sequence were defined as peaks with a preset signal intensity or higher. Furthermore, a uniform value was defined for the frequency width in this case, or a statistical width obtained through learning was used. False detections may have occurred because the exclusion frequency sequences [E] and [E1] were not accurately set using these definitions. Additionally, there remains the possibility that there are problems with the criteria used by the diagnostic device 50 when diagnosing an abnormality.

[0102] When the diagnostic device 50 diagnoses the electric motor 3 or the power transmission mechanism 60, it monitors the signal strength of a characteristic frequency corresponding to the abnormal location and determines that there is an abnormality if the signal strength changes beyond a preset threshold compared to normal data. This criterion (threshold) can be a preset value or a statistical signal strength obtained through learning. False detections may occur because this criterion (threshold) has not been set correctly. Even when false detections occur due to these setting problems of the diagnostic device 50, the database can be used. However, in this case, the user needs to input whether the diagnostic result is correct or incorrect into the input unit 51C of the monitoring and diagnostic unit 51 of the diagnostic device 50.

[0103] As described above, each motor diagnostic device 50 outputs to the database device 100, via the network input / output unit 51G, the above-mentioned exclusion frequency sequences [E], [E1], etc., as well as the criteria for signal intensity when extracting spectral peaks or the criteria for setting the spectral peak width, the criteria for signal intensity used when determining an abnormality, and information on the correctness of the diagnostic results. The diagnostic algorithm modification device 110 at the manufacturer can analyze this information stored in the database device 100 and modify the diagnostic algorithm so that each motor diagnostic device 50 revises these criteria to prevent false detections.

[0104] As described above, according to Embodiment 5, The aforementioned monitoring and diagnostic unit, The specifications information of the power converter, the electric motor, and the power transmission mechanism input to the input unit, The monitoring frequency band, the feature frequency sequence, and the exclusion frequency sequence calculated by the analysis unit, The peaks of the current spectra compared by the determination unit, The determination result indicated by the determination unit, The system now includes a network input / output unit that outputs at least one of the data sets to an external database device.

[0105] Furthermore, the facility is equipped with multiple motor diagnostic devices. The database device to which each of the aforementioned electric motors' diagnostic devices is connected, The system is now equipped with a diagnostic algorithm modification device that is connected to the database device and the diagnostic device for each of the electric motors, and modifies the diagnostic algorithm of the diagnostic device for each of the electric motors.

[0106] Furthermore, each of the aforementioned electric motor diagnostic devices receives modification data from the diagnostic algorithm modification device via the network input / output unit and modifies the diagnostic algorithm of each of the aforementioned electric motor diagnostic devices.

[0107] Furthermore, the items entered by the user in the input unit are modified using the modification data transmitted from the diagnostic algorithm modification device.

[0108] Furthermore, the diagnostic device for each electric motor is configured to modify the content it outputs to the database device via the network input / output unit using the modification data from the diagnostic algorithm modification device.

[0109] Furthermore, the correct / incorrect information regarding the determination result shown by the determination unit is input to the input unit. When the network input / output unit outputs the specifications information of the power converter, the electric motor, and the power transmission mechanism, the monitorable frequency band and the characteristic frequency sequence, and the exclusion frequency sequence to the outside, We have added information regarding the correctness of the judgment result.

[0110] In each of the embodiments described above, the monitoring and diagnostic unit 51 of the diagnostic device 50 is composed of a processor 1000 and a storage device 1010, as shown in Figure 26 as an example of the hardware. The storage device 1010 includes a volatile storage device such as random access memory (not shown) and a non-volatile auxiliary storage device such as flash memory. Alternatively, a hard disk may be provided as an auxiliary storage device instead of flash memory. The processor 1000 executes a program input from the storage device 1010. In this case, the program is input from the auxiliary storage device to the processor 1000 via the volatile storage device. The processor 1000 may also output data such as calculation results to the volatile storage device of the storage device 1010, or it may save the data to the auxiliary storage device via the volatile storage device.

[0111] Although this application describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but can be applied individually or in various combinations to the embodiments. Accordingly, countless variations not illustrated are conceivable within the scope of the art disclosed herein. These include, for example, modifying, adding or omitting at least one component, or even extracting at least one component and combining it with components of other embodiments. [Explanation of Symbols]

[0112] 3 Electric motor, 20 Power converter, 50 Diagnostic device, 51 Monitoring and diagnostic unit, 51A Detection unit, 51B Analysis unit, 51C Input unit, 51D Setting unit, 51E Memory unit, 51F Judgment unit, 51G Network input / output unit, 60 Power transmission mechanism, 100 Database device, 110 Diagnostic algorithm modification device.

Claims

1. A motor diagnostic device for diagnosing at least one of the following: an abnormality in a motor driven by power converted by a power converter, and an abnormality in a power transmission mechanism connected to the motor, The diagnostic device is A current detector for detecting the current flowing from the power converter to the electric motor, The system includes a monitoring and diagnostic unit that performs spectral analysis based on the current detected by the current detector and monitors for at least one abnormality in the electric motor and an abnormality in the power transmission mechanism. The aforementioned monitoring and diagnostic unit, At a minimum, the input unit inputs the following parameters for calculating the modulated wave frequency f0 of the power converter, the number of pole pairs p of the electric motor, the rated rotational speed or rotational speed Nr as parameter information for calculating the slip s of the electric motor, and, if the power transmission mechanism exists, the drive pulley radius Dr and belt length L. Based on the information from the input unit, the frequency components appearing in the current spectrum due to the abnormal modes of the electric motor, namely mechanical system malfunctions and rotor bar damage, and at least one abnormality in the power transmission mechanism, Regarding the aforementioned mechanical system anomaly, the sideband fm' of the modulated wave frequency f0 (fm' = ((1-s) / p)f0), Regarding the rotor bar damage, the sideband fr' of the modulated wave frequency f0 (fr' = 2 * s * f0), Regarding the abnormality in the power transmission mechanism, the sideband fb' of the modulated wave frequency f0 (fb' = ((2・π・Dr) / L)・fr: fr is the rotational frequency of the motor calculated from the rotational speed Nr), A setting unit calculates a characteristic frequency sequence by calculating it as follows, and calculates a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specifications information of the power converter, An analysis unit that performs spectral analysis based on the current detected by the current detector to obtain a current spectrum, extracts a sequence of peak frequencies having spectral peaks with a preset signal intensity or higher in the monitoring frequency range from the current spectrum, compares the peak frequency sequence with the feature frequency sequence and the noise frequency sequence to derive the frequencies included in the noise frequency sequence and the frequencies not included in the feature frequency sequence from the peak frequency sequence as a sequence of frequencies to be excluded from determination, derives the frequencies included in the feature frequency sequence from the peak frequency sequence as a sequence of monitored feature frequencies, and derives a monitorable frequency band by subtracting the sequence of frequencies to be excluded from determination from the monitoring frequency range. A storage unit that stores the aforementioned monitorable frequency band, the aforementioned monitoring characteristic frequency sequence, and its signal strength, A diagnostic device for an electric motor, comprising a determination unit that determines at least one of the following abnormalities: an abnormality in the electric motor and an abnormality in the power transmission mechanism, by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the memory unit with a current spectrum newly acquired from the current detector.

2. Before the diagnostic device diagnoses at least one of the following abnormalities: an abnormality in the electric motor and an abnormality in the power transmission mechanism, The analysis unit stores the monitorable frequency band as normal data, the characteristic frequency sequence and its signal intensity in the storage unit. When the diagnostic device diagnoses at least one of the abnormalities of the electric motor and the power transmission mechanism, The determination unit reads the monitorable frequency band and the feature frequency sequence and its intensity as normal data from the storage unit and compares it with the current spectrum newly acquired from the current detector. The motor diagnostic device according to claim 1, which determines an abnormality when the signal intensity of the monitoring feature frequency sequence included in the monitoring frequency band changes beyond a preset threshold compared to normal data.

3. When the diagnostic device diagnoses an abnormality in the electric motor, The input unit receives the specifications information of the power converter and the electric motor, The setting unit calculates the noise frequency sequence using the specifications information of the power converter input to the input unit, and calculates the characteristic frequency sequence using the specifications information of the power converter and the electric motor input to the input unit. The analysis unit classifies each spectral peak in the monitoring frequency range from the current spectrum acquired by the current detector into the noise frequency sequence, the feature frequency sequence, and the unclassifiable sequence. The noise frequency sequence and the unclassifiable frequency sequence are defined as the frequency sequence to be excluded from determination. The diagnostic device for an electric motor according to claim 2, wherein the monitorable frequency band is derived from the sequence of frequencies excluded from determination.

4. When the diagnostic device diagnoses an abnormality in the power transmission mechanism, The input unit receives the specifications information of the power converter, the electric motor, and the power transmission mechanism. The setting unit calculates the noise frequency sequence using the specifications information of the power converter input to the input unit, and calculates the characteristic frequency sequence using the specifications information of the power converter, the motor, and the power transmission mechanism input to the input unit. The analysis unit classifies each spectral peak from the current spectrum acquired by the current detector into the noise frequency sequence, the feature frequency sequence, and the unclassifiable sequence. The noise frequency sequence and the unclassifiable frequency sequence are defined as the frequency sequence to be excluded from determination. The motor diagnostic device according to claim 2, which calculates the monitorable frequency band from the sequence of frequencies excluded from determination.

5. The aforementioned analysis unit, When checking the settings of the monitoring feature frequency sequence and the judgment exclusion frequency sequence, The load factor of the electric motor is changed and the current spectrum is obtained using the current detector. It was confirmed that the peaks of the current spectrum corresponding to each component of the monitoring characteristic frequency sequence shift depending on the load factor of the motor. A diagnostic device for an electric motor according to any one of claims 2 to 4, wherein if a shift cannot be confirmed, the component of the monitoring feature frequency sequence corresponding to the peak of the current spectrum in which the shift cannot be confirmed is removed from the monitoring feature frequency sequence and added to the judgment exclusion frequency sequence.

6. The aforementioned analysis unit, A diagnostic device for an electric motor according to any one of claims 2 to 4, which proposes changing the settings of the specifications information of the power converter when the ratio of the monitorable frequency band to the monitoring frequency range is less than or equal to a preset ratio threshold.

7. The aforementioned analysis unit, The motor diagnostic device according to claim 5, which proposes changing the settings of the specifications information of the power converter when the ratio of the monitorable frequency band to the monitoring frequency range is less than or equal to a preset ratio threshold.

8. The aforementioned monitoring and diagnostic unit, The specifications information of the power converter, the electric motor, and the power transmission mechanism input to the input unit, The monitoring frequency band, the feature frequency sequence, and the exclusion frequency sequence calculated by the analysis unit, The peaks of the current spectra compared by the determination unit, The determination result indicated by the determination unit, A diagnostic device for an electric motor according to any one of claims 2 to 4, comprising a network input / output unit that outputs at least one of the data to an external database device.

9. The aforementioned monitoring and diagnostic unit, The specifications information of the power converter, the electric motor, and the power transmission mechanism input to the input unit, The monitoring frequency band, the feature frequency sequence, and the exclusion frequency sequence calculated by the analysis unit, The peaks of the current spectra compared by the determination unit, The determination result indicated by the determination unit, The motor diagnostic device according to claim 5, further comprising a network input / output unit that outputs at least one of the data to an external database device.

10. The aforementioned monitoring and diagnostic unit, The specifications information of the power converter, the electric motor, and the power transmission mechanism input to the input unit, The monitoring frequency band, the feature frequency sequence, and the exclusion frequency sequence calculated by the analysis unit, The peaks of the current spectra compared by the determination unit, The determination result indicated by the determination unit, The motor diagnostic device according to claim 6, further comprising a network input / output unit that outputs at least one of the data to an external database device.

11. A plurality of the motor diagnostic devices described in claim 8 are provided, The database device to which each of the aforementioned electric motors' diagnostic devices is connected, A motor diagnostic system comprising a database device and a diagnostic device for each of the motors, and a diagnostic algorithm modification device for modifying the diagnostic algorithm of each of the motors' diagnostic devices.

12. The motor diagnostic system according to claim 11, wherein each of the motor diagnostic devices receives modification data from the diagnostic algorithm modification device via the network input / output unit and modifies the diagnostic algorithm of each of the motor diagnostic devices.

13. The electric motor diagnostic system according to claim 12, wherein the items entered by the user in the input unit are modified by the modification data transmitted from the diagnostic algorithm modification device.

14. The motor diagnostic system according to claim 12, wherein the contents output by each of the motor diagnostic devices to the database device via the network input / output unit are modified by the modification data of the diagnostic algorithm modification device.

15. The correct / incorrect information regarding the determination result shown by the determination unit is input to the input unit. When the network input / output unit outputs the specifications information of the power converter, the electric motor, and the power transmission mechanism, the monitorable frequency band and the characteristic frequency sequence, and the exclusion frequency sequence to the outside, The motor diagnostic system according to claim 12, which adds correct / incorrect information to the aforementioned determination result.

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