DIAGNOSTIC DEVICE FOR ELECTRIC MOTOR AND DIAGNOSTIC SYSTEM FOR ELECTRIC MOTOR

Abstract

The diagnostic device for the electric motor comprises a storage unit (51E) which stores a monitorable frequency band [F] and a monitoring characteristic frequency sequence [G] and their signal strength, and a determination unit [51F] which determines an anomaly of the electric motor (3) and / or an anomaly of a power transmission mechanism (60) by comparing the monitorable frequency band [F] and the monitoring characteristic frequency sequence [G] stored in the storage unit (51E) with a current spectrum newly acquired by a current detector (4).

Description

TECHNICAL AREA

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

[0002] When an anomaly, abnormality, or irregularity is diagnosed in an electric motor driven by a current-based pulse-width modulation (PWM) control system of a power conversion device, compared to a motor driven by a commercial power source, a number of spectral peaks originating from the power conversion device may appear in the frequency spectrum used for anomaly diagnosis. False detection is possible if the frequencies of these spectral peaks overlap with characteristic frequencies used to detect anomalies in the electric motor.

[0003] To address this problem, patent document 1 discloses, for example, a technique in which noise, theoretically predicted based on specification information of the electric motor and the power conversion device, is calculated and compared with spectral peaks extracted during an anomaly diagnosis to avoid false detection.

[0004] This means that the diagnostic device of patent document 1 comprises a detection unit that detects a current flowing into the electric motor, an analysis unit that performs a frequency analysis on the detected current and outputs an analysis result, and a determination unit that identifies an anomaly based on a spectral peak of at least one sideband component in a modulation wave obtained from the analysis result. Furthermore, a frequency setting unit is provided to preset noise frequencies. 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 preset noise frequency and makes a decision regarding the electric motor anomaly. QUOTE LIST PATENT DOCUMENT

[0005] Patent document 1: Japanese patent no. 6824494 SUMMARY OF THE INVENTION PROBLEM TO BE SOLVED BY THE INVENTION

[0006] In conventional technology, a frequency tuning unit theoretically calculates noise frequencies using the modulation wave frequency, carrier frequency, sampling frequency, and power supply frequency, and excludes these frequencies from those used for anomaly diagnosis. However, in actual measurements, unpredictable noise may occur in addition to the theoretically calculated values, which poses a problem with conventional technology.

[0007] The present disclosure provides a technique for solving the problem above and aims to provide a diagnostic device and a diagnostic system for an electric motor that can diagnose an anomaly of the electric motor with high accuracy even in the presence of noise that is not theoretically predictable. MEANS TO SOLVE THE PROBLEM

[0008] The diagnostic device for the electric motor, disclosed in the present disclosure, diagnoses an anomaly of the electric motor, which is driven by power converted by a power conversion device, and / or an anomaly of a power transmission mechanism connected to the electric motor.

[0009] The diagnostic device includes a current detector that can detect a current flowing from the power conversion device to the electric motor, and a monitoring diagnostic unit that performs a spectrum analysis based on the current detected by the current detector to monitor for an anomaly of the electric motor and / or an anomaly of the power transmission mechanism.

[0010] The monitoring diagnostic unit includes: an input unit that inputs at least one specification information of the power conversion device and the electric motor; a setting unit which, based on information from the input unit, calculates a characteristic frequency sequence used to diagnose an anomaly of the electric motor and / or an anomaly of the power transmission mechanism, a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specification information of the power conversion device; an analysis unit that performs a spectral analysis based on the current detected by the current detector to obtain a current spectrum, that extracts from the current spectrum a peak frequency sequence that has a spectral peak greater than or equal to a predetermined signal strength in the monitoring frequency range, and that compares the peak frequency sequence with the characteristic frequency sequence and the noise frequency sequence to derive an exclusion-determining frequency sequence and a monitoring characteristic frequency sequence, and that derives a monitorable frequency band by subtracting the exclusion-determining frequency sequence from the monitoring frequency range; a storage unit that stores the monitorable frequency band, the monitoring characteristic frequency sequence and its signal strength; and a determination unit that determines at least one anomaly from an anomaly of the electric motor and from an anomaly of the power transmission mechanism by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the storage unit with a current spectrum newly obtained from the current detector.

[0011] The diagnostic system for the electric motor disclosed in the present disclosure comprises a plurality of diagnostic devices for the electric motor, a database device to which each of the diagnostic devices for the electric motor is connected, and a diagnostic algorithm modification device which is connected to the database device and each of the diagnostic devices for the electric motor and which modifies a diagnostic algorithm of each of the diagnostic devices for the electric motor. IMPACT OF THE INVENTION

[0012] According to the diagnostic device for an electric motor and the diagnostic system for an electric motor disclosed herein, an anomaly of the electric motor can be diagnosed with high accuracy, even if there is noise that cannot be theoretically predicted. KRUZE DESCRIPTION OF THE DRAWINGS [ Fig. 1] Fig. Figure 1 represents a block diagram showing an overview of a power conversion device and a diagnostic device for an electric motor according to embodiment 1. [ Fig. 2] Fig. Figure 2 represents a circuit diagram showing a main circuit unit for the power conversion device according to embodiment 1. [ Fig. 3] Fig. Figure 3 shows a diagram that provides an overview of the operation or operating mode of a control circuit unit of the power conversion device according to embodiment 1. [ Fig. 4] Fig. Figure 4 shows a block diagram illustrating a monitoring diagnostic unit of the diagnostic device for an electric motor according to embodiment 1. [ Fig. 5] Fig. Figure 5 shows a diagram illustrating a learning process of the diagnostic device for an electric motor according to embodiment 1. [ Fig. 6] Fig. Figure 6 shows a diagram illustrating a learning process of the diagnostic device for an electric motor according to embodiment 1. [ Fig. 7A] Fig. Figure 7A shows a diagram illustrating an example of a frequency spectrum obtained when the electric motor is driven according to embodiment 1. [ Fig. 7B] Fig. Figure 7B presents a schematic diagram showing a monitorable frequency band [F] obtained by subtracting an exclusion frequency sequence [E] from a monitoring frequency range [B]. [ Fig. 8] Fig. Figure 8 shows a diagram illustrating a monitoring diagnostic sequence of the diagnostic device for an electric motor according to embodiment 1. [ Fig. 9] Fig. Figure 9 shows a diagram illustrating a monitoring diagnostic sequence of the diagnostic device for an electric motor according to embodiment 1. [ Fig. 10] Fig. Figure 10 represents a block diagram showing an overview of a power conversion device and a diagnostic device for an electric motor according to embodiment 1. [ Fig. 11] Fig. Figure 11 represents a block diagram showing a monitoring diagnostic unit of the diagnostic device for an electric motor according to embodiment 2. [ Fig. 12] Fig. Figure 12 shows a diagram illustrating a learning process of the diagnostic device for an electric motor according to embodiment 2. [ Fig. 13] Fig. Figure 13 shows a diagram illustrating a learning process of the diagnostic device for an electric motor according to embodiment 2. [ Fig. 14A] Fig. Figure 14A shows a diagram illustrating an example of a frequency spectrum obtained when the electric motor is driven according to embodiment 1. [ Fig. 14B] Fig. Figure 14B shows a schematic diagram illustrating a monitorable frequency band [F1] obtained by subtracting an exclusion frequency sequence [E1] from a monitoring frequency range [B1]. [ Fig. 15] Fig. Figure 15 shows a diagram illustrating a monitoring diagnostic sequence of the diagnostic device for an electric motor according to embodiment 2. [ Fig. 16] Fig. Figure 16 shows a diagram illustrating a monitoring diagnostic sequence of the diagnostic device for an electric motor according to embodiment 2. [ Fig. 17] Fig. Figure 17 shows a diagram illustrating a confirmation sequence of a monitoring characteristic frequency sequence [G] according to embodiment 3. [ Fig. 18] Fig. Figure 18 shows a diagram illustrating a confirmation sequence of a monitoring characteristic frequency sequence [G] according to embodiment 3. [ Fig. 19] Fig. Figure 19 presents a diagram showing a process that proposes a change in the setting conditions of the power conversion device according to embodiment 4. [ Fig. 20] Fig. Figure 20 shows a diagram illustrating a process that proposes a change in the setting conditions of the power conversion device according to embodiment 4. [ Fig. 21] Fig. Figure 21 shows a diagram illustrating a process that proposes a change in the setting conditions of the power conversion device according to embodiment 4. [ Fig. 22A] Fig. Figure 22A presents a diagram showing an example of a frequency spectrum obtained when the electric motor is driven before the setting conditions of the power conversion device are changed according to embodiment 4. [ Fig. 22B] Fig. Figure 22B presents a schematic diagram showing a monitorable frequency band [F1] obtained by subtracting an exclusion frequency sequence [E1] from a monitoring frequency range [B1]. [ Fig. 23A] Fig. Figure 23A presents a diagram showing an example of a frequency spectrum obtained when the electric motor is driven after the setting conditions of the power conversion device have been changed according to embodiment 4. [ Fig. 23B] Fig. Figure 23B presents a schematic diagram showing a monitorable frequency band [F1] obtained by subtracting an exclusion frequency sequence [E1] from a monitoring frequency range [B1]. [ Fig. 24] Fig. Figure 24 shows a block diagram illustrating a monitoring diagnostic unit of the diagnostic device for an electric motor according to embodiment 5. [ Fig. 25] Fig. Figure 25 shows a block diagram illustrating a diagnostic system for an electric motor according to embodiment 5. [ Fig. 26] Fig. Figure 26 presents a diagram showing a hardware configuration example of a monitoring diagnostic unit of the diagnostic device for an electric motor in each embodiment. DESCRIPTION OF THE EXECUTION FORMS Execution form 1

[0013] Fig. Figure 1 represents a block diagram showing an overview configuration of a power conversion device and a diagnostic device for an electric motor according to embodiment 1.

[0014] As in Fig. As shown in Figure 1, the power conversion device 20 converts the frequency of the AC power from an AC power source 1, such as a commercial power source, and supplies the converted power to an electric motor 3. A diagnostic device 50 detects at least one phase of a current supplied by the power conversion device 20 to the electric motor 3 using a current detector 4 and analyzes the detected current to diagnose an anomaly in the electric motor 3. The current detector 4 could be integrated into the power conversion device 20 or it could be mounted externally.

[0015] The power conversion device 20 comprises a main circuit unit 21 that converts the frequency of a power, a control circuit unit 22 that operates the main circuit unit 21, and a power conversion device setting unit 23 that determines the settings of the control circuit unit 22.

[0016] The diagnostic device 50 comprises a monitoring diagnostic unit 51, which monitors and diagnoses an anomaly in the electric motor 3 based on the current detected by the current detector 4; a display unit 52, which displays the results obtained by the monitoring diagnostic unit 51; and an alarm unit 53, which issues an alarm when the monitoring diagnostic unit 51 detects an anomaly. A network output unit could be provided in the display unit 52 and the alarm unit 53, enabling this information to be presented to a user remotely.

[0017] Fig. Figure 2 represents a circuit diagram showing a main circuit unit of the power conversion device according to embodiment 1.

[0018] As in Fig. As shown in Figure 2, the main circuit unit 21 comprises 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 electric motor 3.

[0019] Converter 21A is configured as a three-phase bridge circuit with six diodes Da, with each phase input line connected to the AC power source 1. Inverter 21C is configured as a three-phase bridge circuit comprising six switching elements Q, each of which has an antiparallel-connected diode Db, with each phase output line connected to the electric motor 3. The switching elements Q could be, for example, insulated-gate bipolar transistors (IGBTs) or metal-oxide-semiconductor field-effect transistors (MOSFETs).

[0020] 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 conversion device 20, which are defined by the power conversion device setting unit 23.

[0021] It should be noted that the configurations of the converter 21A and the inverter 21C are not limited to those shown. While the main circuit unit 21 of the power conversion device 20 is described as comprising the converter 21A and connected to the AC power source 1, it is also sufficient for the inverter 21C, which converts DC power into AC power and supplies it to the electric motor 3, to be included in the system; the converter 21A could thus be omitted.

[0022] Furthermore, in the present example, the AC power source 1, the power conversion device 20 and the electric motor 3 are shown by way of example with a three-phase configuration, the disclosure being not limited thereto.

[0023] Fig. Figure 3 shows a diagram that provides an overview of the operation of a control circuit unit of the power conversion device according to embodiment 1.

[0024] The control circuit unit 22 of the power conversion device 20 controls the main circuit unit 21 of the power conversion device 20 using a pulse-width modulation (PWM) method. The PWM method modulates the frequency of the power supplied to the electric motor 3 by extracting short-duration voltage pulses from the DC voltage of the smoothing capacitor 21B. This short-duration extraction is achieved by rapidly controlling the ON / OFF states of the switching elements Q of the inverter 21C. A control signal G for controlling the switching elements Q is generated in the control circuit unit 22 by a modulation wave generation unit 22A, a signal discretization unit 22B, and a signal extraction unit 22C.

[0025] The modulation wave generation unit 22A oscillates a sine wave at a user-defined modulation wave frequency f0. The signal discretization unit 22B samples the modulation wave output by the modulation wave generation unit 22A at a frequency fs and acquires discrete data. The control circuit unit 22 compares the discrete data with a carrier frequency fc (e.g., a triangle wave) generated by the signal extraction unit 22C and produces the control signal G.

[0026] During the process of generating the control signal G, which is in Fig. As shown in Figure 3, noise occurs at a frequency that is an integer multiple of the greatest common divisor of the modulation wave frequency f0, the sampling frequency fs, and the carrier frequency fc, i.e., the GCD (f0, fs, fc). Such noise is called Fpwm and is defined by equation (1). Fpwn=i⋅GCD(f0,fs,fc)

[0027] Here, i is a positive integer. For example, if f0 = 60 Hz, fs = 4000 Hz, and fc = 2000 Hz, the greatest common divisor (GCD) of f0, fs, and fc is 20 Hz, resulting in Fpwm values ​​of 20 Hz, 40 Hz, 60 Hz, ....

[0028] Such low-frequency noise overlaps with the frequency spectrum range monitored by the diagnostic device 50 and therefore must be processed appropriately.

[0029] During the process in which the converter 21A of the power conversion device 20 generates a DC voltage, noise is also generated that overlaps with the frequency band monitored by the diagnostic device 50. This noise is defined as Fv by equation (2). Fv=|m⋅fac±n⋅f0|

[0030] Here, fac is the frequency of the power supplied by the 21A converter, and m and n are positive integers. For example, if f0 = 60 Hz and fac = 50 Hz, then Fv becomes 10 Hz, 20 Hz, 30 Hz, .... Such low-frequency noise overlaps with the frequency spectrum range monitored by the diagnostic device 50 and therefore must be processed appropriately.

[0031] Furthermore, harmonic components or overtone components of the modulation wave are generated, which are integer multiples of the modulation wave frequency. These are defined as F0 by equation (3). F0=k⋅f0 Here, k is a positive integer.

[0032] Other noise generated by the power conversion device 20 includes: Noise caused by the dead time provided to prevent damage to the switching elements when the control signal G is generated; Noise resulting from overmodulation due to the amplitude conditions of the modulation wave and the carrier wave; and Noise generated during the extraction of the control signal G. If any of these noise types overlap with the frequency band monitored by the diagnostic device 50, they must be processed appropriately.

[0033] The current detector 4 detects the current generated by the power conversion device 20. The detected current is processed by the diagnostic device 50. First, the diagnostic device 50 learns the characteristics of the electric motor 3 operating under normal conditions. This means that it performs a spectral analysis of the electric motor 3's current in normal operating conditions and stores the resulting characteristics. When a diagnosis is performed, the diagnostic device 50 compares the detected current with the learned characteristics.

[0034] If an anomaly occurs in electric motor 3, deviations in frequency will appear according to each anomaly mode. Mechanical anomalies and rotor bar damage in electric motor 3 are described below as examples. [Mechanical anomalies]

[0035] If a mechanical anomaly occurs in the electric motor 3, rotor vibration and eccentricity will occur. This eccentricity causes periodic changes or fluctuations in the air gap length between the rotor and the stator. Periodic fluctuations in the air gap length alter the electrical properties of the electric motor 3, causing slight disturbances in the motor current, which is detected by the current detector 4. If a spectral analysis is performed on these disturbances, sideband waves f0 ± fm' of the modulation wave frequency f0 can be observed. Here, fm' is defined by equation (4). fm'=((1−s) / p)⋅f0

[0036] Here, p is the number of pole pairs and s is the slip. [Rotor rod damage]

[0037] If rotor bar damage occurs in the electric motor 3, a negative-phase component (-s·f0) is generated in the current flowing through the rotor bars. This negative-phase component causes a current of frequency (1 - s)·f0 to be induced in the stator. This current results in torque oscillations with a frequency of 2s·f0 and induces magnetic flux oscillations with a frequency of (1 ± 2s)·f0. Consequently, slight disturbances occur in the current, which is detected by the current detector 4. If a spectral analysis is performed on these disturbances, sideband waves f0 ± fr' of the modulation wave frequency f0 can be observed. Here, fr' is defined by equation (5). fr'=2⋅s⋅f0

[0038] Furthermore, the slip s is defined by an equation (6) based on the rotational speed N0 of the rotating magnetic field and the rotational speed Nr of the rotor. s=(N0−Nr) / N0

[0039] Furthermore, the slip s of the electric motor 3 can also be estimated approximately, since the rotational speed Nr of the rotor can be estimated approximately from the rated speed of the electric motor 3 and the rotational speed N0 of the rotating magnetic field is defined using the number of pole pairs p and the modulation wave frequency f0, as shown in equation (7). N0=(60 / p)⋅f0

[0040] The sideband waves associated with mechanical anomalies and rotor bar damage, as expressed in equations (4) and (5), are present to some extent in the frequency spectrum detected by the current detector 4, even when the electric motor 3 is operating normally. However, their signal intensity increases significantly when an anomaly occurs. The diagnostic device 50 diagnoses the anomaly of the electric motor 3 based on this increase in signal intensity.

[0041] Fig. Figure 4 represents a diagram showing a block diagram of the monitoring diagnostic unit of the diagnostic device for an electric motor according to embodiment 1. Fig. 5 and Fig. Figure 6 shows diagrams illustrating the learning process of the diagnostic device for an electric motor according to embodiment 1.

[0042] As in Fig. As shown in Figure 4, the monitoring diagnostic unit 51 of the diagnostic device 50 comprises 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. Each function during the learning phase is based on the learning sequence in the Fig. 5 and Fig. 6 will be described.

[0043] As in the Fig. 5 and Fig. As shown in Figure 6, the diagnostic device 50 performs the following steps to diagnose anomalies in the electric motor 3.

[0044] First, in step S10, specification information for the target electric motor 3 and the power conversion device 20 (such as the number of pole pairs of the electric motor 3, a rated speed, an input power frequency fac of the power conversion device 20, a modulation wave frequency f0, a carrier frequency fc, and a sampling frequency fs) is entered into the input unit 51C. This specification information could be entered directly into the input unit 51C of the diagnostic device 50 or transmitted remotely via a network.

[0045] Next, the setting unit 51D calculates a characteristic frequency sequence [A] based on the input specification information, which is used to diagnose anomalies in the electric motor 3 in step S11, based on the previously mentioned equations (4) and (5).

[0046] Furthermore, in step S12, the setting unit 51D calculates a monitoring frequency range [B], which is used to monitor the characteristic frequency sequence [A].

[0047] The monitoring frequency range [B] is determined based on equations (4) and (5). For example, considering the conditions in the Fig. 7A and Fig. 7B, where f0 = 40 Hz and p = 1, and monitoring an electric motor 3 with a slip s of 2% to 10%, the following frequencies will be obtained: f0+fm=76 Hz to 79.2 Hz f0−fm=0.8 Hz to 4 Hz f0+fr=41.6 Hz to 48 Hz f0−fr=32 Hz to 38.4 Hz

[0048] To ensure that these frequencies are monitored, the monitoring frequency range [B] is set or defined accordingly. The range information for the slip s could be obtained from the datasheet of the electric motor 3, estimated, or specified as part of the product specifications of the diagnostic device 50.

[0049] Next, in step S13, the setting unit 51D calculates a noise frequency sequence [C] that is expected to be generated within the monitoring frequency range [B], based on equations (1) to (3).

[0050] The setting unit 51D then transmits the calculated characteristic frequency sequence [A], the monitoring frequency range [B] and the noise frequency sequence [C] to the analysis unit 51B.

[0051] Meanwhile, in step S14, the current detector 4 records the current of the electric motor 3. The recorded current of the electric motor 3 is digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B.

[0052] Next, in step S15, the analyzer 51B performs a spectral analysis of the current signal received by the sensing unit 51A using a Fast Fourier Transform (FFT) or similar methods. In step S16, the analyzer 51B extracts the frequencies and signal intensities from spectral peaks.

[0053] Then, in step S17, the analysis unit 51B stores the peak frequency sequence [D] of the spectral peaks within the monitoring frequency range [B] in the storage unit 51E. Here, the peak frequency sequence [D] is a frequency sequence that has spectral peaks that exceed a preset signal intensity, for example -65 dB or higher.

[0054] Next, in steps S18 to S22, the analysis unit 51B compares the characteristic frequency sequence [A], the monitoring frequency range [B], and the noise frequency sequence [C], which can be derived from the specification information transmitted by the setting unit 51D, with the extracted peak frequency sequence [D] and classifies the extracted peak frequency sequence [D] based on its respective sources of generation. At this time, the diagnostic device 50 excludes the frequencies included in the peak frequency sequence [D] that cannot be classified in the noise frequency sequence [C] from the monitoring frequency range [B] used for anomaly diagnosis, thereby determining a monitorable frequency band [F] that is stored in the storage unit 51E.The width of the spectral peaks encompassed by the exclusion frequency sequence [E] could be set based on the statistical width obtained during the learning period or uniformly set to a fixed value, such as a few Hz. In the present embodiment, an example is shown where the width is set to ±1.5 Hz.

[0055] The details of steps S18 to S22 are explained below. In step S18, the analysis unit 51B determines whether the peak frequency sequence [D] is encompassed by the computable noise frequency sequence [C]. If the peak frequency sequence [D] is encompassed by the computable noise frequency sequence [C] in step S18, the process proceeds to step S20, where it is stored as the exclusion frequency sequence [E] in the storage unit 51E.

[0056] If the peak frequency sequence [D] is not encompassed by the computable noise frequency sequence [C] in step S18, the process proceeds to step S19, where it is determined whether the peak frequency sequence [D] is encompassed by the characteristic frequency sequence [A]. If the peak frequency sequence [D] is not encompassed by the characteristic frequency sequence [A] in step S19, the process proceeds to step S20, where it is stored as the exclusion frequency sequence [E] in memory unit 51E.

[0057] In step S20, once the exclusion frequency sequence [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 exclusion frequency sequence [E] from the monitoring frequency range [B] and storing it in the storage unit 51E.

[0058] On the other hand, if the peak frequency sequence [D] is encompassed by the characteristic frequency sequence [A] in step S19, the process proceeds to step S22, where it is stored as the monitoring characteristic frequency sequence [G] in the storage unit 51E.

[0059] Fig. Figure 7A shows a diagram illustrating an example of a frequency spectrum obtained when the electric motor is driven. Fig. Figure 7B presents a schematic diagram showing the monitorable frequency band [F] obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B].

[0060] Fig. Figure 7A shows an example of a frequency spectrum obtained by the analysis unit 51B of the monitoring diagnostic unit 51 under the conditions where f0 (modulation wave frequency) = 40 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 2000 Hz and fs (sampling frequency) = 4000 Hz.

[0061] The monitoring frequency range [B] for anomaly detection is set, for example, to 0 Hz to 100 Hz.

[0062] In this case as well, a frequency sequence exhibiting spectral peaks with a signal intensity greater than or equal to -65 dB, which is a preset value, is considered the peak frequency sequence [C]. The Fpwm calculated by equation (1) is an integer multiple of 40 Hz, which is the same as F0 determined by equation (3).

[0063] As in Fig. As shown in image 7A, spectral peaks at 40 Hz and 80 Hz are confirmed.

[0064] The Fv calculated by equation (2) results in 20 Hz, 40 Hz, 60 Hz, 80 Hz and 100 Hz, and the in Fig. The spectral peaks observed in 7A also appear at 20 Hz, 40 Hz, 60 Hz, 80 Hz, and 100 Hz. Frequencies that match those predicted by theoretical formulas are stored as the computable noise frequency sequence [C] in the exclusion frequency sequence [E]. Furthermore, spectral peaks in the 10–18 Hz and 21–30 Hz ranges are confirmed, which are not explained by equations (1) to (3) or the characteristic frequency sequence [A]. Since these spectral peaks overlap with the characteristic frequency sequence [A] during anomaly diagnosis and could potentially cause error captures, they are stored in the exclusion frequency sequence [E].

[0065] Fig. Figure 7B schematically shows the monitorable frequency band [F] obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B].

[0066] During the learning period, the diagnostic device 50 stores the monitorable frequency band [F] in the memory unit 51E. Fig. 7B is the monitored frequency band [F] shown as the empty area obtained by subtracting the exclusion frequency sequence [E] from the monitoring frequency range [B].

[0067] If mechanical anomalies or rotor bar damage occur in the electric motor 3 with one pole pair, the frequency ranges used to diagnose these anomalies are within the monitorable frequency band [F] in Fig. 7B is specified as [P1] and [P2] for mechanical anomalies and as [Q1] and [Q2] for rotor bar damage. Here, the slip range for mechanical anomalies is set to 4% to 20%, and the slip range for rotor bar damage is set to 2% to 12%.

[0068] The above confirms that the method disclosed in the present specification enables the detection of mechanical anomalies and rotor bar damage under these conditions of use, while avoiding the influence of noise.

[0069] The Fig. 8 and Fig. Figure 9 shows diagrams illustrating the monitoring and diagnostic process of the diagnostic device for an electric motor.

[0070] In step S30, the determination unit 51F of the diagnostic device 50, which has completed the learning process, reads the monitorable frequency band [F] and the monitoring characteristic frequency sequence [G], which are stored in the memory unit 51E.

[0071] Then, in step S31, as in the learning process, the current of the electric motor 3 is detected by the current detector 4. The detected current is digitized by the acquisition unit 51A and sent as a current signal to the analysis unit 51B.

[0072] In step S32, the analysis unit 51B performs a spectral analysis of the current signal using a Fast Fourier Transform (FFT) or a similar method.

[0073] In step S33, the analysis unit 51B extracts the frequencies and signal intensities of the spectral peaks and sends them to the determination unit 51F.

[0074] Next, in step S34, the determination unit 51F extracts spectral peaks that fall within the monitored frequency band [F] from those received by the analysis unit 51B.

[0075] In step S35, the determination unit 51F determines whether the signal intensity of the spectral peaks within the monitored frequency band [F] changes beyond a preset intensity threshold compared to spectral peaks obtained during the learning phase.

[0076] If the signal intensity change exceeds the preset threshold, the process proceeds to step S36, where it is determined whether the frequency of the spectral peak that has changed beyond the intensity threshold matches the monitoring characteristic frequency sequence [G].

[0077] If the signal intensity change does not exceed the preset threshold, the process either returns to step S31, where the current of the electric motor 3 is again detected by the current detector 4, or the anomaly diagnosis process is terminated.

[0078] Furthermore, if no significant change is detected in step S35, the determination unit 51F stores a determination result indicating normal operation in the storage unit 51E and displays this information on the display unit 52.

[0079] In step S36, if it is determined that the frequency of the spectral peak matches or corresponds to the monitoring characteristic frequency sequence [G], the process proceeds to step S37, where the anomaly location is identified.

[0080] In this stage, the frequency of the spectral peak is inputted into the previously mentioned equations (4) and (5) to determine whether the anomaly is a mechanical anomaly or rotor bar damage. The process then proceeds to step S38.

[0081] In step S38, the determination result from step S37 is stored in the storage unit 51E, displayed on the display unit 52 of the diagnostic device 50, and an alarm is issued via the alarm unit 53.

[0082] In step S36, if it is determined that the frequency of the spectral peak does not match the monitoring characteristic frequency sequence [G], the process proceeds to step S38. In this case, although the intensity change of the spectral peak does not correspond to a mechanical anomaly or rotor bar damage, it is determined to be an unusual condition. The determination result is stored in the memory unit 51E, displayed on the display unit 52 of the diagnostic device 50, and an alarm is issued via the alarm unit 53.

[0083] Furthermore, the display unit 52 and the alarm unit 53 of the diagnostic device 50 could be equipped with a network output unit to provide such information to the user remotely.

[0084] According to embodiment 1, a diagnostic device is provided for diagnosing an anomaly of a motor driven by a power that is converted by a power conversion device.

[0085] The diagnostic device includes a current detector that detects a current flowing from the power conversion device to the electric motor, and a monitoring diagnostic unit that performs a spectrum analysis based on the current detected by the current detector to monitor for an anomaly of the electric motor.

[0086] The monitoring diagnostic unit includes: an input unit that inputs specification information for the power conversion device and / or the electric motor; a setting unit that calculates, based on information from the input unit, a characteristic frequency sequence used to diagnose an anomaly of the electric motor, a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specification information of the power conversion device; an analysis unit that performs a spectral analysis based on the current detected by the current detector to obtain a current spectrum, that extracts from the current spectrum a peak frequency sequence with a spectral peak greater than or equal to a predetermined signal strength in the monitoring frequency range, and that compares the peak frequency sequence with the characteristic frequency sequence and the noise frequency sequence to derive an exclusion-determining frequency sequence and a monitoring characteristic frequency sequence, and that derives a monitorable frequency band by subtracting the exclusion-determining frequency sequence from the monitoring frequency range; a storage unit that stores the monitorable frequency band, the monitoring characteristic frequency sequence and its signal strength; and a determination unit that identifies an anomaly of the electric motor by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the memory unit with a current spectrum newly obtained from the current detector.

[0087] Furthermore, before the diagnostic device diagnoses an anomaly in the electric motor, The analysis unit stores, as normal data, the monitorable frequency band and the characteristic frequency sequence and their signal strength in the storage unit, and, when the diagnostic device diagnoses the electric motor anomaly, The determination unit reads the monitored frequency band and the characteristic frequency sequence and their signal strength, as the normal data, from the storage unit, compares them with a current spectrum newly acquired by the current detector, and determines an anomaly if the signal strength of the monitoring characteristic frequency sequence encompassed by the monitored frequency band changes beyond a preset threshold compared to the normal data.

[0088] Furthermore, if the diagnostic device diagnoses the anomaly of the electric motor, The input unit enters specification information for the power conversion device and the electric motor. The setting unit calculates the noise frequency sequence using the specification information of the power conversion device entered into the input unit, and calculates the characteristic frequency sequence using the specification information of the power conversion device and the electric motor entered into the input unit. The analysis unit classifies each spectrum peak within the monitoring frequency range of the current spectrum captured by the current detector into the noise frequency sequence, the characteristic frequency sequence, and an unclassifiable frequency sequence. defines the noise frequency sequence and the unclassifiable frequency sequence as the exclusion frequency sequence, and The monitorable frequency band is derived from the exclusion frequency sequence.

[0089] By setting up the system as described above in embodiment 1, it is possible to diagnose the motor anomaly with high accuracy, even in the presence of unpredictable noise. Design 2

[0090] Fig. Figure 10 shows a block diagram illustrating the general configuration of a power conversion device and a motor diagnostic device according to embodiment 2.

[0091] As in Fig. As shown in Figure 10, the power conversion device 20 converts the frequency of the AC power from the AC power supply 1 and feeds the converted power to the motor 3. The motor 3 is connected to a load 7 via a power transmission mechanism 60. The diagnostic device 50 detects at least one phase of the current supplied to the motor 3 by the power conversion device 20 using a current detector 4 and analyzes the detected current to detect anomalies in the motor 3 and in the motor 3's power transmission mechanism 60.

[0092] In embodiment 1, the diagnostic device 50 diagnoses mechanical anomalies of the motor 3 and rotor bar damage, whereas in embodiment 2 it also diagnoses the power transmission mechanism 60 connected to the motor 3.

[0093] It should be noted that the diagnosis of mechanical anomalies and rotor bar damage of the motor 3, as described in embodiment 1, can be easily combined with the diagnosis of the power transmission mechanism 60 of the motor 3, as described in embodiment 2.

[0094] The following description focuses on the differences compared to embodiment 1, and similar aspects will be omitted.

[0095] As in Fig. As shown in Figure 10, the motor 3 is connected to the load 7 via the power transmission mechanism 60. The power transmission mechanism 60 comprises, for example, a belt 61 that connects a disk Pu1, which is connected to the rotating shaft of the motor 3, and a disk Pu2, which is attached to the rotating shaft of the load 7 and transmits the power of the motor 3 to the load 7. If the radius of disk Pu1 is Dr, if the length of belt 61 is L, and if the rotational speed of motor 3 is Nr, then the length of the belt rotated by Pu1 per unit of rotation is 2π × Dr × Nr. The ratio of this length to the total length L of the belt determines the rotational frequency fb' of the belt, which is obtained as follows: fb'=((2π×Dr) / L)×fr

[0096] Here, represents the rotational frequency of the motor 3.

[0097] When the tape rotates at a frequency fb' while connected to the rotating shaft of motor 3, which rotates at a frequency fr, its oscillation is transmitted through the rotating shaft of motor 3 to the rotor and appears as sideband frequencies f0 ± fb' in the current frequency spectrum, which is detected by the current detector 4. Furthermore, overtones or harmonics also appear, such as f0 ± 2fb', f0 ± 3fb', etc.

[0098] These sideband frequencies are observed with high signal intensity when the band 61 is cleanly connected to the disk Pu1. However, if damage occurs, such as a break, the signal intensity decreases. Since this behavior is the opposite of that described for mechanical anomalies and rotor bar damage in embodiment 1, a partially different procedure is required to diagnose the power transmission mechanism 60.

[0099] Fig. Figure 11 shows a block diagram illustrating the structure of the monitoring diagnostic unit of the engine diagnostic device according to embodiment 2, and Fig. 12 and Fig. Figure 13 illustrates the learning process of the engine diagnostic device according to embodiment 2.

[0100] As in Fig. As shown in Figure 11, the monitoring diagnostic unit 51 of the diagnostic device 50 comprises 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 these components during the learning process will be explained based on the learning sequences described in the Fig. 12 and Fig. 13 are shown.

[0101] Fig. 12 and Fig. Figure 13 illustrates the learning process of the engine diagnostic device according to embodiment 2.

[0102] First, in step S110, the specifications of the motor 3, the power conversion device 20, and the power transmission mechanism 60 are entered into the input unit 51C. In embodiment 1, only the specifications of the motor 3 and the power conversion device 20 were entered, whereas in embodiment 2, additional specifications of the power transmission mechanism 60 (such as the radius Dr of the disk Pu1, the length L of the belt 61, and the rotational speed Nr of the motor 3) are included.

[0103] Next, based on the input specifications, the setting unit 51D calculates the characteristic frequency sequence [A1] used to diagnose anomalies in the motor 3 and the power transmission mechanism 60 in step S111 based on the previously mentioned equations (4), (5) and (8).

[0104] In step S112, the setting unit 51D calculates the monitoring frequency range [B1], which is used to monitor the characteristic frequency sequence [A1]. The monitoring frequency range [B1] is determined based on equations (4), (5) and (8).

[0105] Furthermore, in step S113, the setting unit 51D calculates a noise frequency sequence [C1] that is theoretically predictable and that may appear within the monitoring frequency range [B1], based on the previously mentioned equations (1) to (3).

[0106] The setting unit 51D then sends the calculated characteristic frequency sequence [A1], the monitoring frequency range [B1] and the noise frequency sequence [C1] to the analysis unit 51B.

[0107] Meanwhile, in step S114, the current detector 4 detects the current of the motor 3. The detected current of the motor 3 is digitized by the detection unit 51A and sent as a current signal to the analysis unit 51B.

[0108] Next, in step S115, the analysis unit 51B performs a spectral analysis, such as a Fast Fourier Transform (FFT), on the current signal sent by the detection unit 51A.

[0109] In step S116, the analysis unit 51B extracts the frequencies and signal intensities of the spectral peaks.

[0110] Then, in step S117, the analysis unit 51B stores the peak frequency sequence [D1] within the monitoring frequency range [B1] in the storage unit 51E.

[0111] Next, in steps S118 to S122, the analysis unit 51B compares the peak frequency sequence [D1] with the frequencies transmitted by the setting unit 51D, namely the characteristic frequency sequence [A1], the monitoring frequency range [B1], and the noise frequency sequence [C1], and classifies the extracted peak frequency sequence [D1] according to its sources of generation. During this process, the diagnostic device 50 determines a monitoring frequency range [F1] for the computable noise frequency sequence [C1] and the unclassifiable frequency sequence, which is the frequency of the spectrum peak that cannot be classified, by excluding these frequency sequences from the monitoring frequency range [B1] used when performing anomaly diagnosis, and stores the monitoring frequency range [F1] in the storage unit 51E.The bandwidth of the spectral peaks in the exclusion frequency sequence [E1] to be excluded could be set using a statistical width obtained during the learning period, or could be uniformly set or fixed to a specific value, for example, a few Hz. In the present embodiment, a case is shown as an example where the bandwidth is ±1.5 Hz.

[0112] Details of steps S118 to S122 are described below.

[0113] In step S118, the analysis unit 51B determines whether the peak frequency sequence [D1] is encompassed by the computable noise frequency sequence [C1]. If it is, the process proceeds to step S120 and the frequency is stored in the storage unit 51E as the exclusion frequency sequence [E1].

[0114] In step S118, if the peak frequency sequence [D1] is not encompassed by the computable noise frequency sequence [C1], the process proceeds to step S119, where it is determined whether the peak frequency sequence [D1] is encompassed by the characteristic frequency sequence [A1]. If it is not, the process proceeds to step S120 and the peak frequency sequence [D1] is stored in memory unit 51E as the exclusion frequency sequence [E1].

[0115] After the exclusion frequency sequence [E1] is stored in the memory unit 51E, the process proceeds to step S121, where the monitorable frequency band [F1] is calculated by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1], and it is stored in the memory unit 51E.

[0116] On the other hand, if the peak frequency sequence [D1] is encompassed by the characteristic frequency sequence [A1] in step S119, the process proceeds to step S122 and the peak frequency sequence [D1] is stored in the memory unit 51E as the monitoring characteristic frequency sequence [G1].

[0117] Fig. Figure 14A shows a diagram illustrating an example of the frequency spectrum obtained when the motor is driven according to embodiment 2. Fig. Figure 14B schematically illustrates the monitorable frequency band [F1] obtained by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1]. It should be noted that in Fig. 14B the monitored frequency band [F1] represents the empty area obtained by subtracting the exclusion frequency sequence [E1] from the monitoring frequency range [B1].

[0118] Fig. Figure 14A illustrates an example of a frequency spectrum obtained by the current detector 4 when the motor 3 is driven under the conditions of f0 (modulation wave frequency) = 40 Hz, fac (power supply frequency) = 60 Hz, fc (carrier frequency) = 2000 Hz and fs (sampling frequency) = 4000 Hz.

[0119] The following description explains the differences compared to embodiment 1, while the same content as in embodiment 1 (for example, the descriptions of the Fig. 7A and Fig. 7B) may be omitted.

[0120] For example, it is assumed that the monitoring frequency range [B1] for anomaly monitoring is set to 0-100 Hz. Under the above conditions, if the power transmission mechanism 60 comprises a belt 61 of 1 m length rotating with a disk Pu1 having a radius of 10 cm, and if the number of pole pairs of the rotating motor 3 is 1 with a slip of 5%, the sideband frequencies observed during the diagnosis of the power transmission mechanism 60 appear in ascending order as 16.12 Hz, 63.88 Hz, 87.75 Hz, etc.

[0121] If you compare that in Fig. In the frequency spectrum shown in Figure 14A, with the sideband frequencies observed during the diagnosis of the force transmission mechanism 60, it is evident that 87.75 Hz falls within the monitorable frequency band [F1]. In other words, this means that when the force transmission mechanism 60 is connected, the sideband frequencies at 16.12 Hz and 63.88 Hz, which are shown in Figure 14A, are within the monitorable frequency band [F1]. Fig. The frequencies shown in Figure 14A are not diagnosable due to significant noise, whereas 87.75 Hz is diagnosable because there is little to no noise. This frequency is calculated based on a 5% slip, but if the slip changes between 1% and 16%, the corresponding frequency range is denoted as [R] in Figure 14A. Fig. 14B represents. This means that [R] in Fig. 14B represents the frequency range used to diagnose the power transmission mechanism. Therefore, even with load fluctuations, it is possible to reliably monitor peaks for diagnosing the power transmission mechanism.

[0122] Furthermore, in the Fig. 14A and Fig. 14B, as in the Fig. 7A and Fig. 7B, the frequency ranges [P1] and [P2] for diagnosing mechanical anomalies as well as the frequency ranges [Q1] and [Q2] for diagnosing rotor bar damage are specified.

[0123] The Fig. 15 and Fig. Figure 16 illustrates the monitoring diagnostic procedure of the engine diagnostic device according to embodiment 2.

[0124] In step S130, the determination unit 51F of the diagnostic device 50, which has completed the previously described learning process, reads the monitorable frequency band [F1] and the monitored characteristic frequency sequence [G1], which are stored in the memory unit 51E. Similar to the learning process, in step S131 the current detector 4 records the current of the motor 3. The recorded current is then digitized by the acquisition unit 51A and sent as a current signal to the analysis unit 51B.

[0125] Next, in step S132, the analysis unit 51B performs a spectral analysis, such as a Fast Fourier Transform (FFT), on the current signal received by the detection unit 51A.

[0126] Then, in step S133, the analysis unit 51B extracts the spectral peak frequencies and signal intensities and sends them to the determination unit 51F.

[0127] In step S134, the determination unit 51F extracts the spectral peaks that fall within the monitored frequency band [F1] from those sent by the analysis unit 51B.

[0128] In step S135, it is determined whether the signal intensity of the spectral peaks within the monitored frequency band [F1] changes beyond a preset intensity range compared to the spectral peaks recorded during the learning process.

[0129] If the change exceeds the preset intensity range in step S135, step S136 follows, where it is determined whether the frequency of the spectral peaks whose intensity has changed beyond the range matches the monitored characteristic frequency sequence [G1].

[0130] If the change does not exceed the intensity range in step S135, the process either returns to step S131 for the next anomaly diagnosis, where current detector 4 again detects the motor current, or terminates the motor anomaly diagnosis.

[0131] Furthermore, if no significant change in intensity is detected in step S135, the determination result indicating normal operation is stored in the memory unit 51E and displayed as “normal” on the display unit 52.

[0132] In step S136, if it is determined that the spectral peak frequency matches the monitored characteristic frequency sequence [G1], step S137 follows, where the anomaly location is determined. Here, the spectral peak frequency is applied to the previously mentioned equations (4) and (5) to determine whether the anomaly is caused by a mechanical problem or rotor bar damage. The spectral peak frequency is also applied to the previously mentioned equation (8) to determine whether the anomaly is due to the power transmission mechanism. The process then proceeds to step S138.

[0133] In step S138, the anomaly location result from step S137 is stored in the storage unit 51E, displayed on the display unit 52 of the diagnostic device 50, or an alarm is triggered via the alarm unit 53.

[0134] If, in step S136, it is determined that the frequency of the spectral peak does not correspond to the monitoring characteristic frequency sequence [G1], the process proceeds to step S138. In this case, although the change in the signal intensity of the spectral peak does not correspond to any of the anomalies, such as a mechanical anomaly, rotor bar damage, or an anomaly of the power transmission mechanism, it is considered an unusual condition. The determination result is stored in the memory unit 51E, displayed on the display unit 52 of the diagnostic device 50, or announced via the alarm unit 53.

[0135] Furthermore, the network output unit could be provided in the display unit 52 and the alarm unit 53 of the diagnostic device 50, making it possible to present this information to a user remotely.

[0136] As mentioned earlier, when diagnosing the power transmission system 60, an anomaly is detected when the signal intensity of the characteristic frequency sequence [G1] decreases. This differs from the diagnosis of mechanical anomalies and rotor bar damage described in embodiment 1. Accordingly, the present embodiment and embodiment 1 can be easily combined.

[0137] As described above according to embodiment 2, the electric motor diagnostic device diagnoses the anomaly of the electric motor driven by power converted by the power conversion device and the anomaly of the power transmission mechanism connected to the electric motor.

[0138] The diagnostic device comprises a current detector that detects a current flowing from the power conversion device to the electric motor, and a monitoring diagnostic unit that performs a spectrum analysis based on the current detected by the current detector in order to detect at least one anomaly of the electric motor and / or an anomaly of the power transmission mechanism.

[0139] The monitoring diagnostic unit includes: an input unit that inputs at least one specification information of the power conversion device and / or the electric motor; a setting unit which, based on information from the input unit, calculates a characteristic frequency sequence used to diagnose an anomaly of the electric motor and / or an anomaly of the power transmission mechanism, a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specification information of the power conversion device; an analysis unit that performs a spectral analysis based on the current detected by the current detector to obtain a current spectrum, that extracts from the current spectrum a peak frequency sequence with a spectral peak greater than or equal to a predetermined signal strength in the monitoring frequency range, and that compares the peak frequency sequence with the characteristic frequency sequence and the noise frequency sequence to derive an exclusion frequency sequence and a monitoring characteristic frequency sequence, and that derives a monitorable frequency band by subtracting the exclusion frequency sequence from the monitoring frequency range; a storage unit that stores the monitorable frequency band, the monitoring characteristic frequency sequence and its signal strength; and a determination unit that determines at least one anomaly from an anomaly of the electric motor and an anomaly of the power transmission mechanism by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the storage unit with a current spectrum newly obtained from the current detector.

[0140] Furthermore, before the diagnostic device diagnoses the anomaly of the electric motor and the anomaly of the power transmission mechanism, it stores the analysis unit, as normal data, the monitorable frequency band and the characteristic frequency sequence and their signal strength in the storage unit, and, when the diagnostic device diagnoses the anomaly of the electric motor and the anomaly of the power transmission mechanism, The determination unit reads the monitored frequency band and the characteristic frequency sequence and their signal strength, as the normal data, from the storage unit, compares them with a current spectrum newly acquired by the current detector, and determines an anomaly if the signal strength of the monitoring characteristic frequency sequence encompassed by the monitored frequency band changes beyond a preset threshold compared to the normal data.

[0141] Furthermore, if the diagnostic device diagnoses anomalies in the engine and the power transmission mechanism, The input unit enters specification information for the power conversion device, the electric motor, and the power transmission mechanism. The setting unit calculates the noise frequency sequence using the specification information of the power conversion device entered into the input unit, and calculates the characteristic frequency sequence using the specification information of the power conversion device, the electric motor and the power transmission mechanism entered into the input unit. The analysis unit classifies each spectrum peak of a spectrum of a current detected by the current detector into the noise frequency sequence, the characteristic frequency sequence, and an unclassifiable frequency sequence. defines the noise frequency sequence and the unclassifiable frequency sequence as the exclusion frequency sequence, and The monitorable frequency band is calculated from the exclusion frequency sequence.

[0142] By setting up embodiment 2 as described above, even in the presence of noise that cannot be theoretically predicted, anomalies in the engine and in the power transmission system can be diagnosed with high precision. embodiment 3

[0143] In embodiment 3, in order to more clearly distinguish the exclusion-determination frequency sequence [E] and the monitoring characteristic frequency sequence [G] in embodiment 1, as well as the exclusion-determination frequency sequence [E1] and the monitoring characteristic frequency sequence [G1] in embodiment 2, the learning process of the diagnostic device 50 is performed while the load factor of the motor 3 is changed. This approach utilizes the fact that the characteristic frequencies of mechanical anomalies, rotor bar damage, and the power transmission mechanism, as specified by the aforementioned formulas (4), (5), and (8), depend on a slip s.

[0144] The Fig. 17 and Fig. Figure 18 illustrates the verification process for the monitoring characteristic frequency sequence [G] in embodiment 3.

[0145] The Fig. 17 and Fig. 18 describe the process in which a diagnostic device 50 performs a learning process while the load factor of the motor 3 is changed in order to distinguish more clearly between the exclusion determination frequency sequence [E1] and the monitoring characteristic frequency sequence [G1] in embodiment 2. Changing the load factor of motor 3 means, for example, adjusting the valve of a pump connected to motor 3, thereby changing the load factor.

[0146] As in Fig. As shown in Figure 13 of embodiment 2, the exclusion determination frequency sequence [E1] and the monitoring characteristic frequency sequence [G1] are stored in a storage unit 51E.

[0147] In step S200, an analysis unit 51B reads the exclusion determination frequency sequence [E1] and the monitoring characteristic frequency sequence [G1], which are stored in a storage unit 51E.

[0148] Next, the load factor of motor 3 is changed in step S201 and in step S202 the current is detected by a current detector 4.

[0149] Then, in step S203, an analysis unit 51B performs a spectral analysis of the captured current signal using a Fast Fourier Transform (FFT) or similar techniques.

[0150] Subsequently, an analysis unit 51B extracts the frequencies and signal intensities of the spectral peaks in step S204.

[0151] Next, in step S205, an analysis unit 51B compares the spectral peaks obtained in step S204 with the monitoring characteristic frequency sequence [G1].

[0152] Since the monitoring characteristic frequency sequence [G1] is a characteristic frequency calculated based on equations (4), (5), and (8), changes in the load factor appear as fluctuations in slip, which can shift the corresponding characteristic frequencies. If no shift is observed (No in step S206), then these frequencies are not considered characteristic frequencies. In step S207, frequencies that do not shift are added to the exclusion frequency sequence [E1] and removed from the monitoring characteristic frequency sequence [G1].

[0153] This procedure allows the exclusion determination frequency sequence [E1] and the monitoring characteristic frequency sequence [G1] to be distinguished more clearly.

[0154] As described above, according to embodiment 3, when the analysis unit confirms the setting of the monitoring characteristic frequency sequence and the exclusion determination frequency sequence, The analysis unit changes a load factor of the electric motor and records the current spectrum with the current detector. confirms that a peak in the current spectrum shifts according to each component of the monitoring characteristic frequency sequence depending on the load factor of the electric motor, and, If the offset cannot be confirmed, it excludes the component of the monitoring characteristic frequency sequence corresponding to the peak of the current spectrum for which the offset cannot be confirmed from the monitoring characteristic frequency sequence and adds it to the exclusion determination frequency sequence.

[0155] Therefore, the monitoring characteristic frequency sequence and the exclusion determination frequency sequence can be more clearly distinguished. Design 4

[0156] In embodiment 1, the diagnostic device 50 performed the diagnosis of the motor 3 by inputting the specification information of the motor 3 and the power conversion device 20.

[0157] In embodiment 2, the diagnostic device 50 performs the diagnosis of the motor 3 and the power transmission mechanism 60 by inputting the specification information of the motor 3, the power conversion device 20, and the power transmission mechanism 60. In each embodiment, the diagnostic device 50 learns the computable noise frequency sequence generated by the power conversion device 20 and ensures that the diagnosis is performed while excluding the computable noise frequency sequence. However, the noise generation characteristics of the power conversion device 20 depend on its setting conditions. As a result, if excessive noise is generated, the monitorable frequency band [F] or [F1] could become too narrow, making it difficult to reliably perform anomaly diagnosis.

[0158] Embodiment 4 addresses this problem.

[0159] The Fig. 19, Fig. 20 and Fig. Figure 21 illustrates the procedure for proposing changes to the setting conditions of the power conversion device 20 in embodiment 4. The procedures in the Fig. 19, Fig. 20, and Fig. 21 follow the learning process in embodiment 2, as described in the Fig. 12 and Fig. 13 shown.

[0160] In the Fig. 19, Fig. 20 and Fig. In the sequence shown in section 21, steps S310 to S322 are the same as steps S110 to S122 in the Fig. 12 and Fig. 13, so that their explanation is omitted.

[0161] During the course of the Fig. 19, Fig. 20 to Fig. In steps S321 and S322, the monitored frequency band [F1] and the monitored characteristic frequency sequence [G1] are stored in a memory unit 51E. At this time, if the monitored frequency band [F1] is narrower than the monitored frequency range [B1], it could be difficult to perform an anomaly diagnosis.

[0162] Fig. Figure 22A illustrates an example of a frequency spectrum of the current detected by a current detector 4 when the modulation wave frequency f0 is set to 119 Hz, the power supply frequency fac is set to 60 Hz, the carrier frequency fc is set to 1000 Hz, and the sampling frequency fs is set to 4000 Hz. Here, the monitoring frequency range [B1] for anomaly detection is set to 0 Hz to 220 Hz.

[0163] It can be confirmed that spectral peaks occur across the entire monitoring frequency range [B1]. Similar to the examples in the previously described embodiments, spectral peaks with a signal intensity of -65 dB or more are designated as the exclusion frequency sequence [E1] and their width is set to ±1.5 Hz. These frequencies and widths could also be determined based on statistical values ​​obtained through training.

[0164] Fig. Figure 22B illustrates the exclusion frequency sequence [E1] and the monitorable frequency band [F1] in the case of Fig. 22A.

[0165] As in the previous explanation, in Fig. 22B the monitorable frequency band [F1] is specified as the empty areas or regions between the exclusion frequency sequence [E1].

[0166] Out of Fig. As can be seen in Figure 22B, the monitorable frequency band [F1] is narrow relative to the monitoring frequency range [B1]. When the ratio of the monitorable frequency band [F1] to the monitoring frequency range [B1] is evaluated, the result is 29%. Performing anomaly diagnosis within such a narrow range is difficult.

[0167] Therefore, in the present embodiment, a preset ratio threshold is set for the ratio and, if the ratio is less than or equal to the ratio threshold, the diagnostic device 50 suggests changing the setting conditions for the power conversion device 20.

[0168] For example, if the ratio threshold is set to 50% and the ratio of the monitored frequency band [F1] to the monitored frequency range [B1] is below the value in Fig. If the condition shown in 22A is 29%, which is below the threshold, then the diagnostic device 50 suggests changing the settings of the power conversion device 20.

[0169] In step S330, it is determined whether the ratio of the monitored frequency band [F1] to the monitoring frequency range [B1] is less than or equal to the preset ratio threshold.

[0170] If the ratio of the monitored frequency band [F1] to the monitoring frequency range [B1] is less than or equal to the preset ratio threshold in step S330, the process proceeds to step S340, where the setting conditions of the power conversion device 20 are changed.

[0171] Then, with the changed setting conditions of the power conversion device 20, the processes from step S310 to step S330 are repeated.

[0172] If the ratio of the monitored frequency band [F1] to the monitored frequency range [B1] exceeds the preset ratio threshold in step S330, the learning process is complete.

[0173] If a change to the setting conditions of the power conversion device 20 is proposed, the proposal is made in such a way as to minimize the deviation from the original settings in order to avoid a deviation from the intended application of the motor 3. An example is a PWM frequency Fpwm, as expressed in equation (1). Among the in the Fig. 22A and Fig. Under the conditions shown in 22B, if i is a positive integer, Fpwm = i. According to equation (1), Fpwm should appear every 1 Hz, but such spectral peaks in Fig. 22A cannot be observed.

[0174] However, if the greatest common divisor (GCD) of f0, fc, and fs increases, an Fpwm-fulfilling equation (1) becomes clearer in the frequency spectrum, thus reducing the overall noise component. Accordingly, a diagnostic device 50 proposes shifting the modulation wave frequency f0 from 119 Hz to 120 Hz. In this case, Fpwm = 40i and spectral peaks occur every 40 Hz, effectively reducing the noise component.

[0175] Fig. Figure 23A illustrates the frequency spectrum of the current that is detected when the modulation wave frequency f0 is set to 120 Hz, when the power supply frequency fac is set to 60 Hz, when the carrier frequency fc is set to 1000 Hz, and when the sampling frequency fs is set to 4000 Hz. Fig. Figure 23B shows the corresponding exclusion frequency sequence [E1] and the monitorable frequency band [F1].

[0176] As described above, the monitorable frequency band [F1] has expanded relative to the monitoring frequency range [B1]. When the ratio of the monitorable frequency band [F1] to the monitoring frequency range [B1] is evaluated, the result is 77%. Such a wide range enables reliable diagnosis.

[0177] Since the change in the modulation wave frequency f0 is also approximately 1%, it does not deviate from the intended application of motor 3.

[0178] As described above, according to the present embodiment, if the ratio of the monitorable frequency band to the monitoring frequency range is less than or equal to a preset ratio threshold, The analysis unit suggests changing the setting of the specification information of the power conversion device.

[0179] Accordingly, the ratio of the monitorable frequency band to the monitoring frequency range can be expanded, enabling highly accurate anomaly diagnosis of the electric motor and an improvement in reliability. EXECUTION FORM 5

[0180] In embodiments 1 to 4, the motor diagnostic device 50 diagnoses anomalies in the motor 3, which is driven by the power conversion device 20 or by the power transmission mechanism 60 connected to the motor 3. It calculates noise generated by the power conversion device 20 using equations (1), (2), and (3) or learns the noise characteristics for each installation. Such noise generation could also depend on the model number of the power conversion device 20 or the motor 3, or on their installation conditions. Embodiment 5 addresses these issues.

[0181] Fig. Figure 24 shows a block diagram illustrating the monitoring diagnostic unit of the engine diagnostic device according to embodiment 5. Fig. Figure 25 shows a block diagram illustrating the engine diagnostic system according to embodiment 5.

[0182] As in Fig. As shown in Figure 24, the monitoring diagnostic unit 51 of the engine diagnostic device 50 according to embodiment 5 comprises the acquisition unit 51A, the analysis unit 51B, the input unit 51C, the setting unit 51D, the storage unit 51E and the determination unit 51F, which were described in the previous embodiments, and further comprises a network input / output unit 51G which exchanges data with external devices through a network.

[0183] As in Fig. As shown in Figure 25, the engine diagnostic system comprises a database device 100 that accumulates various types of data sent via the network input / output unit 51G from a plurality of engine diagnostic devices 50 (diagnostic device A, diagnostic device B, and diagnostic device C). 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 a plurality of the multiple engine diagnostic devices 50 (diagnostic device A, diagnostic device B, and diagnostic device C) via the network.

[0184] As described in the previous embodiments, each of the motor diagnostic devices 50 (diagnostic device A, diagnostic device B and diagnostic device C) receives the specification information of the power conversion device 20, the motor 3 and the power transmission mechanism 60 according to the learning sequences described in the Fig. 5 and Fig. 6 or in the Fig. 12 and Fig. Figure 13 shows that, based on this specification information, each of them calculates the exclusion determination frequency sequence [E] or [E1], the monitorable frequency band [F] or [F1], and the monitoring characteristic frequency sequence [G] or [G1].

[0185] Furthermore, each engine diagnostic device 50 (diagnostic device A, diagnostic device B, and diagnostic device C) outputs these calculated results and the acquired specification information of the power conversion device 20, the engine 3, or the power transmission mechanism 60 to the database device 100 via the network input / output unit 51G. The output information could additionally include the spectral peaks compared by the determination unit 51F, the determination results of the determination unit 51F, and the model numbers of the power conversion device 20 and the engine 3.

[0186] The database device 100 is preferably installed at a location where data from a variety of engine diagnostic devices 50 can be entered. While it is installed within a single plant to collect data from diagnostic devices within that plant, it is more preferred to install it externally and collect data from diagnostic devices in a variety of plants.

[0187] The data stored in the database device are analyzed by the diagnostic algorithm modification device 110, which is provided by the manufacturer of the diagnostic device 50.

[0188] The analysis by the diagnostic algorithm modification device 110 is performed using the model numbers or setting conditions of the power conversion device 20, the electric motor 3 and the power transmission mechanism 60 stored in the database device 100, the exclusion determination frequency sequence [E] or [E1], the monitorable frequency band [F] or [F1], the monitoring characteristic frequency sequence [G] or [G1], the peaks of the current spectrum compared by the determination unit 51F, the determination results of the determination unit 51F and the like.

[0189] Based on this data, the diagnostic algorithm for each engine diagnostic device 50 is modified.

[0190] After being modified by the diagnostic algorithm modification device 110, the network input / output unit 51G receives the modified data from each monitoring diagnostic unit 51 and the diagnostic algorithms of the individual devices are modified accordingly.

[0191] An example of the modified content is the proposal to change the setting conditions of the power conversion device 20, as described in embodiment 4.

[0192] As explained in embodiment 4, the noise characteristics generated by the power conversion device 20 change depending on the magnitude of the GGT (f0, fc, fs) represented by equation (1). Although the occurrence of certain types of noise can be controlled by setting a GGT (f0, fc, fs), it is difficult to capture the detailed noise generation characteristics, which might depend on the model numbers of the power conversion device 20 or the motor 3. To address this, the diagnostic algorithm is modified to utilize data in the database device 100 when changes to the setting conditions are suggested.

[0193] If a specific power conversion device 20 and a specific motor 3 generate excessive noise at certain settings, making diagnosis difficult at that time in order to fine-tune the settings of the power conversion device 20, it is possible to search for setting values ​​that are assumed to improve noise generation from similar conditions in the data of the database device 100 and to use these as a diagnostic algorithm that is suggested to the user.

[0194] Alternatively, the manufacturer can analyze the database to develop new diagnostic algorithms that improve noise characteristics and install them in each diagnostic device 50. It should be noted that such modified diagnostic algorithms are limited by the specifications of each diagnostic device 50, such as its processing speed or memory capacity.

[0195] Another example of the modified content concerns the content of diagnostic results obtained by the diagnostic device 50 when the motor 3 or the power transmission mechanism 60 is diagnosed, as in the Fig. 8 and Fig. 9 or in the Fig. 15 and Fig. 16 shown.

[0196] As described in embodiments 1 to 4, the motor diagnostic device 50 diagnoses anomalies in the motor 3 or in the power transmission mechanism 60, while preventing noise generated by the power conversion device 20. However, some error captures could inevitably occur during the diagnosis. Such error captures could, for example, result from the method used to define the exclusion-determining frequency sequences [E] and [E1] learned by the diagnostic device 50.

[0197] In embodiments 1 and 2, spectral peaks in the peak frequency sequence were defined as those with a signal intensity greater than or equal to a preset value. Furthermore, the frequency width was defined either as a fixed value or as a statistical width obtained through training. It is possible that errors occurred because the exclusion frequency sequences [E] and [E1] were not precisely determined based on these definitions. Additionally, there could be problems with the criteria used by the diagnostic device 50 to identify anomalies.

[0198] When the motor 3 or the power transmission mechanism 60 is diagnosed, the diagnostic device 50 monitors the signal intensity of the characteristic frequencies corresponding to the anomaly locations and determines the presence of an anomaly if the signal intensity deviates from a preset threshold compared to normal data. This threshold could be either a predetermined fixed value or a statistical value obtained through learning. Error detections could occur if this threshold is not set appropriately. Even if error detections are caused by configuration-related problems of the diagnostic device 50, the database can still be used.However, in such cases it is necessary to equip the input unit 51C of the monitoring diagnostic unit 51 of the diagnostic device 50 with a new function that allows the user to input the correctness of the diagnostic results.

[0199] As described above, each engine diagnostic device 50 transmits, via the network input / output unit 51G, not only the exclusion determination frequency sequence [E] and [E1], but also the criteria for signal intensity used to extract spectral peaks, the criteria for defining spectral peak widths, the criteria for signal intensity used for anomaly detection, and the precise information of the diagnostic results to the database device 100. The diagnostic algorithm modification device 110, located at the manufacturer's site, analyzes the information stored in the database device 100 and modifies the diagnostic algorithm relative to these criteria, so that each engine diagnostic device 50 can avoid fault detections.

[0200] As described above, the monitoring diagnostic unit includes a network input / output unit that outputs data from the specification information of the power conversion device, electric motor and power transmission mechanism, which is entered into the input unit, the monitorable frequency band, the characteristic frequency sequence and / or the exclusion determination frequency sequence calculated by the analysis unit, the peaks of the current spectrum compared by the determination unit and / or the determination result indicated by the determination unit to an external database device.

[0201] Furthermore, a diagnostic system for an electric motor includes: a variety of diagnostic devices for the electric motor; the database device to which each of the diagnostic devices for an electric motor is connected; and a diagnostic algorithm modification device that is connected to the database device and each of the diagnostic devices for the electric motor and that modifies a diagnostic algorithm for each of the diagnostic devices for an electric motor.

[0202] Furthermore, each engine diagnostic device receives the modification data from the diagnostic algorithm modification device via the network input / output unit and modifies its diagnostic algorithm.

[0203] Furthermore, the elements that users enter into the input unit are modified by the modification data transmitted by the diagnostic algorithm modification device.

[0204] Furthermore, each engine diagnostic device modifies content output from the database device via the network input / output unit, based on modification data from the diagnostic algorithm modification device.

[0205] Furthermore, correctness information for the determination result, which is specified by the unit of determination, is entered into the input unit and, when the network input / output unit outputs the specification information of the power conversion device, the electric motor and the power transmission mechanism, the monitored frequency band, the characteristic frequency sequence and the exclusion frequency sequence, The correctness information for the determination result is added.

[0206] In the embodiments described above, the monitoring and diagnostic unit 51 of the diagnostic device 50 consists of a processor 1000 and a storage device 1010, as shown in Fig.Figure 26 shows that the storage device 1010 comprises non-volatile memory, such as read-only memory (RAM), and a non-volatile auxiliary storage device, such as flash memory. Alternatively, a hard disk could be used as the auxiliary storage device instead of flash memory. The processor 1000 executes a program that is loaded from the storage device 1010. In this case, the program is loaded from the auxiliary storage device into the processor 1000 via the volatile memory. The processor 1000 could output computed data to the volatile memory of the storage device 1010. Alternatively, the data could be stored in the auxiliary storage device via the volatile memory.

[0207] Although the disclosure is described here in terms of various exemplary embodiments and implementations, it is understood that the various features, aspects and functionality described in one or more of the individual embodiments are not limited in their applicability to the particular embodiment in which they are described, but instead can be applied alone or in various combinations to one or more of the embodiments of the disclosure.

[0208] It is therefore understood that numerous modifications, not shown by way of example, can be devised without deviating from the scope of the present disclosure. For example, at least one of the component parts could be modified, added, or eliminated. At least one of the component parts mentioned in at least one of the preferred embodiments could be selected and combined with the component parts mentioned in another preferred embodiment. DESCRIPTION OF REFERENCE MARKS 3 Engine 20 Power conversion device 50 diagnostic device 51 Monitoring diagnostic unit 51A Acquisition unit 51B Analysis Unit 51C Input Unit 51D setting unit 51E Storage Unit 51F Unit of determination 51G network input / output unit 60 Power transmission mechanism 100 database device 110 Diagnostic algorithm modification device QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] JP 6824494

[0005]

Claims

[1] Diagnostic device for an electric motor, which diagnoses an anomaly of the electric motor driven by power converted by a power conversion device and / or an anomaly of a power transmission mechanism connected to the electric motor, wherein the diagnostic device comprises: a current detector that detects a current flowing from the power conversion device to the electric motor; and a monitoring diagnostic unit that performs a spectrum analysis based on the current detected by the current detector to monitor for an anomaly in the electric motor and / or an anomaly in the power transmission mechanism, the monitoring diagnostic unit includes: an input unit that inputs specification information for the power conversion device and / or the electric motor; a setting unit which, based on information from the input unit, calculates a characteristic frequency sequence used to diagnose an anomaly of the electric motor and / or an anomaly of the power transmission mechanism, a monitoring frequency range used to monitor the anomaly, and a noise frequency sequence that can be derived from the specification information of the power conversion device; an analysis unit that performs a spectral analysis based on the current detected by the current detector to obtain a current spectrum, that extracts from the current spectrum a peak frequency sequence that has a spectral peak greater than or equal to a predetermined signal strength in the monitoring frequency range, and that compares the peak frequency sequence with the characteristic frequency sequence and the noise frequency sequence to derive an exclusion-determining frequency sequence and a monitoring characteristic frequency sequence, and that derives a monitorable frequency band by subtracting the exclusion-determining frequency sequence from the monitoring frequency range; a storage unit that stores the monitorable frequency band, the monitoring characteristic frequency sequence and its signal strength; and a determination unit that determines at least one anomaly from an anomaly of the electric motor and an anomaly of the power transmission mechanism by comparing the monitorable frequency band and the monitoring characteristic frequency sequence stored in the storage unit with a current spectrum newly obtained from the current detector. [2] Diagnostic device for the electric motor according to claim 1, wherein before the diagnostic device diagnoses at least one anomaly consisting of an anomaly of the electric motor and an anomaly of the power transmission mechanism, The analysis unit stores normal data, the monitorable frequency band, the characteristic frequency sequence, and its signal strength in the storage unit, and if the diagnostic device diagnoses at least one anomaly from the anomaly of the electric motor and the anomaly of the power transmission mechanism, The determination unit reads the monitored frequency band and the characteristic frequency sequence and their signal strength as the normal data from the storage unit, compares them with a current spectrum newly obtained from the current detector, and determines an anomaly if the signal strength of the monitoring characteristic frequency sequence encompassed by the monitored frequency band changes beyond a preset threshold compared to the normal data. [3] Diagnostic device for the electric motor according to claim 2, wherein if the diagnostic device diagnoses an anomaly in the electric motor, the input unit enters specification information for the power conversion device and the electric motor, The setting unit calculates the noise frequency sequence using the specification information of the power conversion device entered into the input unit, and calculates the characteristic frequency sequence using the specification information of the power conversion device and the electric motor entered into the input unit. The analysis unit classifies each spectrum peak within the monitoring frequency range of the current spectrum captured by the current detector into the noise frequency sequence, the characteristic frequency sequence, and an unclassifiable frequency sequence. the noise frequency sequence and the unclassifiable frequency sequence are defined as the exclusion frequency sequence, and derives the monitorable frequency band from the exclusion frequency sequence. [4] Diagnostic device for the electric motor according to claim 2, wherein if the diagnostic device diagnoses an anomaly in the power transmission mechanism, the input unit enters specification information for the power conversion device, the electric motor, and the power transmission mechanism, The setting unit calculates the noise frequency sequence using the specification information of the power conversion device entered into the input unit, and calculates the characteristic frequency sequence using the specification information of the power conversion device, the electric motor, and the power transmission mechanism entered into the input unit. The analysis unit classifies each spectrum peak of a spectrum of a current detected by the current detector into a noise frequency sequence, the characteristic frequency sequence, and an unclassifiable frequency sequence. the noise frequency sequence and the unclassifiable frequency sequence are defined as the exclusion frequency sequence, and The monitorable frequency band is calculated from the exclusion frequency sequence. [5] Diagnostic device for the electric motor according to one of claims 2 to 4, wherein when the analysis unit confirms the setting of the monitoring characteristic frequency sequence and the exclusion determination frequency sequence, the analysis unit changes a load factor of the electric motor and records the current spectrum with the current detector, confirms that a peak in the current spectrum, corresponding to each component of the monitoring characteristic frequency sequence, shifts depending on the load factor of the electric motor, and, If the offset cannot be confirmed, exclude the component of the monitoring characteristic frequency sequence corresponding to the peak of the current spectrum for which the offset cannot be confirmed from the monitoring characteristic frequency sequence, and add it to the exclusion determination frequency sequence. [6] Diagnostic device for the electric motor according to any one of claims 2 to 5, wherein, if the ratio of the monitored frequency band to the monitoring frequency range is less than or equal to a preset ratio threshold, The analysis unit suggests changing the setting of the specification information of the power conversion device. [7] Diagnostic device for the electric motor according to any one of claims 2 to 6, wherein The monitoring diagnostic unit comprises a network input / output unit that outputs data from the specification information of the power conversion device, electric motor and / or power transmission mechanism entered into the input unit, the monitorable frequency band, the characteristic frequency sequence and the exclusion determination frequency sequence calculated by the analysis unit, the peaks of the current spectrum compared by the determination unit, and the determination result indicated by the determination unit to an external database device. [8] Diagnostic system for the electric motor, which includes: a plurality of diagnostic devices for the electric motor according to claim 7; the database device to which each of the diagnostic devices for the electric motor is connected; and a diagnostic algorithm modification device that is connected to the database device and each of the diagnostic devices for the electric motor and that modifies a diagnostic algorithm for each of the diagnostic devices for an electric motor. [9] Diagnostic system for the electric motor according to claim 8, wherein each of the diagnostic devices for an electric motor receives modification data from the diagnostic algorithm modification device via the network input / output unit and modifies the diagnostic algorithm for each of the diagnostic devices for an electric motor. [10] Diagnostic system for the electric motor according to claim 9, wherein elements entered into the input unit by a user are modified by the modification data transmitted by the diagnostic algorithm modification device. [11] Diagnostic system for the electric motor according to claim 9, wherein contents which are output to the database device via the network input / output unit for each of the diagnostic devices for the electric motor are modified by the modification data of the diagnostic algorithm modification device. [12] Diagnostic system for the electric motor according to claim 9, wherein correctness information for the determination result specified by the determination unit is entered into the input unit, and when the network input / output unit outputs the specification information of the power conversion device, the electric motor and the power transmission mechanism, the monitorable frequency band, the characteristic frequency sequence and the exclusion determination frequency sequence, the correctness information for the determination result is added.

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