A rolling bearing fault diagnosis method and system

Abstract

The present invention provides a rolling bearing fault diagnosis method and system, including: step S1, collecting vibration data from different measuring points; step S2, resonant demodulation processing on the vibration data, and then fast Fourier transform processing to obtain an envelope spectrum; step S3, local background noise calculation, and local normalization of the spectrum peak of the envelope spectrum; step S4, quantifying the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic, determining the prominence of the spectrum peak of the normalized envelope average spectrum, and then determining whether there is a fault, wherein N is a natural number greater than or equal to 1. Beneficial effect: The present invention makes the rolling bearing data in any scenario have uniform comparability through the local peak normalization technology. When the characteristic frequency and the Nth harmonic component of the normalized envelope average spectrum have a certain degree of prominence in the demodulated spectrum, the high accuracy of the intelligent diagnosis result is ensured through the diagnosis rules.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing and intelligent diagnosis of rotating machinery, and in particular to a rolling bearing fault diagnosis method and system. Background Art

[0002] At present, in my country's metallurgical, petroleum, chemical and other industries, rotating mechanical equipment accounts for about 80%. Among them, rolling bearings are the main load-bearing and rotating components. Once accidentally damaged, the entire equipment will lose its working ability. The resulting downtime will cause huge losses to the company. Therefore, it is of great significance to monitor and diagnose rolling bearings and realize predictive maintenance.

[0003] At present, the research on the mechanism model based on rolling bearing failure has become mature, mainly through the acceleration sensor on the bearing seat to collect the impact signal generated by the bearing damage, and obtain the fault characteristics through the envelope demodulation technology. For diagnostic engineers, fault diagnosis can be easily performed by analyzing the spectrum and envelope spectrum through the basic parameter information of the bearing. However, for the online monitoring and diagnosis system, it still remains at the stage of monitoring as the main and diagnosis as the auxiliary. Due to differences in functions and models, there are significant differences in the normal working conditions of different equipment. The current method requires monitoring and diagnosis by comparing the characteristic trends of the equipment over a period of time, resulting in several days or weeks of debugging for online system deployment of different types of equipment, which brings great inconvenience to service providers and equipment users. Therefore, how to design an intelligent diagnostic algorithm that can replace diagnostic engineers to the greatest extent has become a difficult problem faced by the current rolling bearing online diagnosis system. Based on the above problems, the present invention proposes a rolling bearing fault diagnosis method and system based on local peak normalization of the spectrum to meet the actual application needs. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a rolling bearing fault diagnosis method and system.

[0005] The technical problem solved by the present invention can be achieved by adopting the following technical solutions:

[0006] A rolling bearing fault diagnosis method, comprising:

[0007] Step S1, collecting vibration data of different measuring points set for each rolling bearing in a rotating device;

[0008] Step S2, performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum;

[0009] Step S3, performing local background noise calculation on the envelope spectrum, and performing local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum;

[0010] Step S4, quantizing the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic to determine the prominence of the spectrum peak of the normalized envelope average spectrum, and then determining whether there is a fault, wherein N is a natural number greater than or equal to 1.

[0011] Preferably, the step S1 specifically includes:

[0012] The vibration acceleration signal of each measuring point is collected according to a preset sampling frequency and used as the vibration data.

[0013] Preferably, the step S2 specifically includes:

[0014] Step S21, performing band-pass filtering on the collected vibration data to obtain first filtered data, and performing high-pass filtering on the vibration data to obtain second filtered data;

[0015] Step S22, performing Hilbert transform and fast Fourier transform on the first filtered data to obtain a first envelope spectrum; and performing Hilbert transform and fast Fourier transform on the second filtered data to obtain a second envelope spectrum.

[0016] Preferably, in step S2, the cutoff frequency of the bandpass filter is 200-2500 Hz;

[0017] The cutoff frequency of the high-pass filter is 2500Hz.

[0018] Preferably, the step S3 specifically includes:

[0019] The amplitudes of the first envelope spectrum and the second envelope spectrum at a preset frequency are respectively normalized to obtain normalized amplitudes, and envelope spectrum averaging is performed on the normalized first envelope spectrum and the second envelope spectrum to obtain the normalized envelope average spectrum.

[0020] Preferably, in step S3, the normalization process is performed using the following formula to obtain the normalized amplitude:

[0021]

[0022]

[0023] Among them, n f represents the spectral valley mean value within a preset frequency band around the preset frequency as the center frequency;

[0024] L represents the number of minima in the spectral valley mean;

[0025] represents the minimum value of the spectral valley mean;

[0026] A f represents the amplitude at the preset frequency;

[0027] represents the normalized amplitude.

[0028] Preferably, in step S3, the preset frequency band is greater than or equal to the inner ring fault characteristic frequency of the rolling bearing.

[0029] Preferably, the step S4 specifically includes:

[0030] Step S41, extracting the first to fourth harmonics of the characteristic frequency from the normalized envelope average spectrum;

[0031] Step S42, calculating the harmonic mean of the four harmonics from the first harmonic to the fourth harmonic;

[0032] Step S43, comparing the harmonic mean value with a preset universal threshold value, and determining that a fault exists when the harmonic mean value exceeds the preset universal threshold value and at least two harmonics from the first harmonic to the fourth harmonic exceed the preset universal threshold value.

[0033] Preferably, in step S4, the preset universal threshold is 6-7.

[0034] The present invention also provides a rolling bearing fault diagnosis system, including the rolling bearing fault diagnosis method as described above, such as Figure 3 As shown, the system comprises:

[0035] A collection unit, used for collecting vibration data of different measuring points set for each rolling bearing in a rotating device;

[0036] a processing unit, connected to the acquisition unit, for performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum;

[0037] a local normalization unit, connected to the processing unit, for performing local noise floor calculation on the envelope spectrum and performing local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum;

[0038] A fault diagnosis unit is connected to the local normalization unit, and is used to quantify the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic to determine the degree of prominence of the spectrum peak of the normalized envelope average spectrum, and then determine whether a fault exists, wherein N is a natural number greater than or equal to 1.

[0039] The advantages or beneficial effects of the technical solution of the present invention are:

[0040] The rolling bearing fault diagnosis method based on the local peak normalization technology of the spectrum proposed in the present invention performs dual-band envelope processing through the resonance demodulation technology to avoid the omission of the resonance frequency band. The local peak normalization technology is used to make the rolling bearing data in any scenario have uniform comparability. When the amplitude of the characteristic frequency after fusion exceeds the universal threshold, it indicates that the component has a certain degree of prominence in the demodulated spectrum. Through certain diagnostic rules, the high accuracy of the intelligent diagnosis results is ensured, that is, less misdiagnosis and less missed diagnosis. It does not need to rely on historical data of the equipment, has strong versatility, can be applied to different working conditions and different types of equipment, can be applied to real-time online monitoring scenarios, and effectively reduces the deployment time caused by parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a schematic diagram of the overall flow of a rolling bearing fault diagnosis method in a preferred embodiment of the present invention;

[0042] Figure 2 A schematic structural diagram of an experimental device used in a rolling bearing fault diagnosis method in a preferred embodiment of the present invention;

[0043] Figure 3 This is a structural block diagram of a rolling bearing fault diagnosis system in a preferred embodiment of the present invention;

[0044] Figure 4a Schematic diagram of the waveform of the vibration acceleration signal in the preferred embodiment 1 of the present invention;

[0045] Figure 4b Schematic diagram of the envelope spectrum of the vibration acceleration signal in the preferred embodiment 1 of the present invention;

[0046] Figure 4c It is a schematic diagram of an envelope average spectrum with local peak normalization in the preferred embodiment 1 of the present invention;

[0047] Figure 5a Schematic diagram of the waveform of the vibration acceleration signal in the preferred embodiment 2 of the present invention;

[0048] Figure 5b Schematic diagram of the envelope spectrum of the vibration acceleration signal in the preferred embodiment 2 of the present invention;

[0049] Figure 5c Schematic diagram of envelope average spectrum with local peak normalization in preferred embodiment 2 of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0052] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0053] The rotating equipment in the embodiment of the present invention can be selected from the rolling bearing test equipment of the American Intelligent Maintenance System (IMS) Center. Figure 2 As shown, it specifically includes: a motor 2, a rolling bearing, and a sensor unit 1; the rolling bearing includes multiple rolling bearings, and the multiple rolling bearings are connected to the motor 2 through a shaft, and the speed of the motor 2 is constantly maintained at 2000RPM; in the embodiment of the present invention, there are specifically 4 rolling bearings, and two sensor units 1 are arranged on each rolling bearing. The sensor unit 1 adopts a PCB353B33 high-sensitivity quartz ICP accelerometer, the first accelerometer is vertically arranged, and the second accelerometer is horizontally arranged; in the embodiment of the present invention, a 6000LB radial load is applied to the shaft and the rolling bearing through a spring mechanism, and all rolling bearings are forced to be lubricated, and each accelerometer is used as a measuring point. By collecting vibration data of each measuring point, and performing fault diagnosis based on the spectrum local peak normalization technology, it is determined whether there is a fault.

[0054] It should be noted that, for the sake of clarity, the embodiments of the present invention are distinguished by rolling bearing A, rolling bearing B, rolling bearing C, and rolling bearing D, indicating that they are arranged at different positions on the shaft. In fact, they can be collectively referred to as rolling bearings. Similarly, the first and second accelerometers can also be collectively referred to as accelerometers.

[0055] An embodiment of the present invention provides a rolling bearing fault diagnosis method, comprising the following steps:

[0056] Step S1, collecting vibration data of different measuring points set for each rolling bearing in a rotating device;

[0057] Step S2, performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum;

[0058] Step S3, performing local noise floor calculation on the envelope spectrum, and performing local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum;

[0059] Step S4, quantizing the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic to determine the prominence of the spectrum peak of the normalized envelope average spectrum, and then determining whether there is a fault, wherein N is a natural number greater than or equal to 1.

[0060] As a preferred implementation, step S1 specifically includes:

[0061] According to a preset sampling frequency f s The vibration acceleration signal of each measuring point is collected and used as vibration data.

[0062] Preferably, the preset sampling frequency f s =2000HZ.

[0063] Preferably, the vibration data is collected by means of the above-mentioned accelerometer or acceleration sensor.

[0064] As a preferred implementation, step S2 specifically includes:

[0065] Step S21, performing band-pass filtering on the collected vibration data to obtain first filtered data, and performing high-pass filtering on the vibration data to obtain second filtered data;

[0066] Step S22, performing Hilbert transform and fast Fourier transform on the first filtered data to obtain a first envelope spectrum; and performing Hilbert transform and fast Fourier transform on the second filtered data to obtain a second envelope spectrum.

[0067] As a preferred embodiment, in step S2, the cutoff frequency of the bandpass filter is 200-2500 Hz;

[0068] The cutoff frequency of the high-pass filter is 2500Hz.

[0069] Furthermore, in step S21, a bandpass filter is used for bandpass filtering, and the cutoff frequency of the bandpass filter is 200-2500 Hz;

[0070] A high-pass filter is used for high-pass filtering, and the cut-off frequency of the high-pass filter is 2500Hz;

[0071] As a preferred implementation, step S3 specifically includes:

[0072] The amplitudes of the first envelope spectrum and the second envelope spectrum at a preset frequency are respectively normalized to obtain normalized amplitudes, and envelope spectrum averaging calculation is performed on the normalized first envelope spectrum and the second envelope spectrum to obtain a normalized envelope average spectrum.

[0073] As a preferred implementation, in step S3, the following formula is used for normalization processing to obtain the normalized amplitude:

[0074]

[0075]

[0076] Among them, n f It represents the mean value of the spectrum valley within a preset frequency band around the preset frequency as the center frequency, that is, the local background noise;

[0077] L represents the spectral valley mean [ff b , f+f b ];

[0078] f represents the preset frequency;

[0079] f b Indicates the preset frequency band;

[0080] represents the minimum value of the spectral valley mean;

[0081] A f represents the amplitude at the preset frequency;

[0082] Represents the normalized magnitude.

[0083] As a preferred implementation, in step S3, the preset frequency band fb is greater than or equal to the inner ring fault characteristic frequency of the rolling bearing.

[0084] Furthermore, when (ff b )≤0 or When L corresponds to the interval [ff b , f+f b ] is adjusted to [1, 2f b +1] and

[0085] As a preferred implementation, step S4 specifically includes:

[0086] Step S41, extracting the first to fourth harmonics of the characteristic frequency from the normalized envelope average spectrum;

[0087] Step S42, calculating the harmonic mean of four harmonics from the first harmonic to the fourth harmonic;

[0088] In step S43, the harmonic mean value is compared with a preset universal threshold value, and when the harmonic mean value exceeds the preset universal threshold value and at least two harmonics from the first harmonic to the fourth harmonic exceed the preset universal threshold value, it is determined that a fault exists.

[0089] As a preferred implementation, in step S4, the preset universal threshold is 6-7.

[0090] Furthermore, the preset universal threshold in the embodiment of the present invention is 6.5.

[0091] The present invention also provides a rolling bearing fault diagnosis system, including the rolling bearing fault diagnosis method as described above, the system comprising:

[0092] A collection unit 3, used to collect vibration data of different measuring points set for each rolling bearing in a rotating device;

[0093] a processing unit 4, connected to the acquisition unit 3, for performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum;

[0094] A local normalization unit 5, connected to the processing unit 4, is used to perform local noise floor calculation on the envelope spectrum and perform local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum;

[0095] A fault diagnosis unit 6 is connected to the local normalization unit 5, and is used to quantify the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic, determine the degree of prominence of the spectrum peak of the normalized envelope average spectrum, and then determine whether there is a fault, wherein N is a natural number greater than or equal to 1.

[0096] Example 1

[0097] In Example 1 of the present invention, the horizontal measuring point of rolling bearing A is used as the analysis object.

[0098] like Figure 1 As shown, the present invention provides a rolling bearing fault diagnosis method, including four steps of data acquisition, signal processing, spectrum editing and fault diagnosis, which are as follows:

[0099] First, the accelerometer 1 is used to sample at a preset sampling frequency f s =2000HZ to collect the vibration acceleration signal of the horizontal measuring point of the rolling bearing A as vibration data; then, the vibration data are subjected to band-pass filtering and high-pass filtering respectively, and the filtered signals are subjected to Hilbert transform and fast Fourier transform respectively to obtain the corresponding envelope spectrum, the cut-off frequency of the band-pass filter is 200-2500Hz, and the cut-off frequency of the high-pass filter is 2500Hz; then, the envelope spectrum obtained above is subjected to spectrum editing, and the amplitude A of the first envelope spectrum and the second envelope spectrum at the preset frequency f is calculated by using the above formulas (1) and (2) respectively. fNormalization processing is performed, and envelope spectrum averaging is performed on the normalized first envelope spectrum and the second envelope spectrum to obtain a normalized envelope average spectrum; finally, fault diagnosis is performed: the 1st to 4th harmonic components of the fault characteristic frequency are extracted from the normalized envelope average spectrum, and according to the diagnosis rule: when the mean value of the 4 harmonics of a certain characteristic frequency exceeds the preset universal threshold, and at least two of the harmonics also exceed the threshold, it is determined that the fault corresponding to the characteristic frequency exists;

[0100] Preferably, the preset universal threshold is 6-7, and in the embodiment of the present invention, it is specifically 6.5.

[0101] Preferably, the rotation frequency of the rotating equipment is fr=2000r / min, and the characteristic coefficients of the outer ring, inner ring, ball and cage of the rolling bearing d are 7.09, 8.91, 4.19 and 0.44 respectively.

[0102] Taking into account the errors between theoretical characteristics and actual characteristics and the existence of fault-free characteristics, the search range of the 1st harmonic envelope spectrum of each characteristic frequency is set to 5Hz, and the search range of the 2nd-4th harmonic envelope spectrum is set to 1Hz.

[0103] See also Figure 4a , is the waveform of the vibration acceleration signal when the fault occurs. It can be seen that the waveform of the vibration acceleration signal contains obvious periodic impact components; see Figure 4b , which is the envelope spectrum of the vibration acceleration signal when the fault occurs. The characteristic frequency and harmonic components of the bearing outer ring fault can be seen in the envelope spectrum.

[0104] See also Figure 4c , is the normalized envelope average spectrum, and its overall spectrum distribution is similar to that before normalization, but after local peak normalization, its overall background noise is below 5. Therefore, setting 6.5 as the general threshold in the embodiment of the present invention is similar to the logic of the spectrum analysis by diagnostic engineers. The spectrum peak that meets the diagnostic rule has a certain degree of prominence in the envelope spectrum.

[0105] Table 1 shows the normalized amplitudes at each characteristic frequency and the 4th harmonic

[0106]

[0107] As shown in Table 1 above, only the outer race fault meets the diagnostic conditions that at least two harmonics are greater than 6.5 and the fourth harmonic mean is greater than 6.5. Specifically, it can be diagnosed as an outer race fault. This result is consistent with the diagnostic engineer's analysis results based on the demodulation spectrum and the actual fault condition of the test bench.

[0108] Example 2

[0109] Using the same rolling bearing experimental equipment and the same four steps of the rolling bearing fault diagnosis method as in Example 1, Example 2 of the present invention uses the horizontal measuring point of the rolling bearing C as the analysis object to perform fault diagnosis.

[0110] See also Figure 5a , which is the waveform of the vibration acceleration signal when the fault occurs. It can be seen that the vibration acceleration signal waveform contains a certain periodic impact component; see Figure 5b , which is the envelope spectrum of the vibration acceleration signal when the fault occurs. The characteristic frequency and harmonic components of the bearing inner ring fault can be seen in the envelope spectrum, which are weaker than the outer ring characteristics.

[0111] See also Figure 5c , is the normalized envelope average spectrum, and its overall spectrum distribution is similar to that of the unnormalized spectrum. However, after local peak normalization processing, the overall background noise is below 5. Therefore, the embodiment of the present invention sets 6.5 as the general threshold, which is similar to the logic of the diagnostic engineer analyzing the spectrum. The spectrum peak that meets this condition has a certain degree of prominence in the envelope spectrum.

[0112] Table 2 shows the normalized amplitudes at each characteristic frequency and the 4th harmonic

[0113]

[0114] As shown in Table 2 above, only the inner race fault meets the diagnostic conditions that at least two harmonics are greater than 6.5 and the fourth harmonic mean is greater than 6.5. Specifically, it can be diagnosed as an inner race fault. This result is consistent with the diagnostic engineer's analysis results based on the demodulation spectrum and the actual fault condition of the test bench.

[0115] The adoption of the above technical scheme has the following advantages or beneficial effects: the rolling bearing fault diagnosis method based on the local peak normalization technology of the spectrum proposed in the present invention performs dual-band envelope processing through the resonance demodulation technology to avoid the omission of the resonance frequency band. The local peak normalization technology is used to make the rolling bearing data in any scenario have uniform comparability. When the amplitude of the characteristic frequency after fusion exceeds the universal threshold, it indicates that the component has a certain degree of prominence in the demodulated spectrum. Through certain diagnostic rules, the high accuracy of the intelligent diagnosis results is ensured, that is, less misdiagnosis and less missed diagnosis; it does not need to rely on historical equipment data, has strong versatility, can be applied to different working conditions and different types of equipment, can be applied to real-time online monitoring scenarios, and effectively reduce the deployment time caused by parameter optimization.

[0116] The above description is only a preferred embodiment of the present invention, and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of this specification and illustrations should be included in the protection scope of the present invention.

Claims

1. A rolling bearing fault diagnosis method, characterized in that: include: Step S1, collecting vibration data of different measuring points set for each rolling bearing in a rotating device; Step S2, performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum; Step S3, performing local background noise calculation on the envelope spectrum, and performing local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum; Step S4, quantifying the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic to determine the prominence of the spectrum peak of the normalized envelope average spectrum, and then determining whether there is a fault, wherein N is a natural number greater than or equal to 1; The step S2 comprises: Step S21, performing band-pass filtering on the collected vibration data to obtain first filtered data, and performing high-pass filtering on the vibration data to obtain second filtered data; Step S22, performing Hilbert transform and fast Fourier transform on the first filtered data to obtain a first envelope spectrum; and performing Hilbert transform and fast Fourier transform on the second filtered data to obtain a second envelope spectrum; The step S3 comprises: The amplitudes of the first envelope spectrum and the second envelope spectrum at a preset frequency are respectively normalized to obtain normalized amplitudes, and envelope spectrum averaging is performed on the normalized first envelope spectrum and the second envelope spectrum to obtain the normalized envelope average spectrum.

2. The rolling bearing fault diagnosis method according to claim 1, characterized in that: The step S1 specifically includes: The vibration acceleration signal of each measuring point is collected according to a preset sampling frequency and used as the vibration data.

3. The rolling bearing fault diagnosis method according to claim 1, characterized in that: In step S2, the cutoff frequency of the bandpass filter is 200-2500 Hz; The cutoff frequency of the high-pass filter is 2500Hz.

4. The rolling bearing fault diagnosis method according to claim 1, characterized in that: In step S3, the normalization process is performed using the following formula to obtain the normalized amplitude: ； ； in, represents the spectral valley mean value within a preset frequency band around the preset frequency as the center frequency; represents the number of minima in the spectral valley mean; represents the minimum value of the spectral valley mean; represents the amplitude at the preset frequency; represents the normalized amplitude.

5. The rolling bearing fault diagnosis method according to claim 4, characterized in that: In step S3, the preset frequency band is greater than or equal to the inner race fault characteristic frequency of the rolling bearing.

6. The rolling bearing fault diagnosis method according to claim 1, characterized in that: The step S4 specifically includes: Step S41, extracting the first to fourth harmonics of the characteristic frequency from the normalized envelope average spectrum; Step S42, calculating the harmonic mean of the four harmonics from the first harmonic to the fourth harmonic; Step S43, comparing the harmonic mean value with a preset universal threshold value, and determining that a fault exists when the harmonic mean value exceeds the preset universal threshold value and at least two harmonics from the first harmonic to the fourth harmonic exceed the preset universal threshold value.

7. The rolling bearing fault diagnosis method according to claim 6, characterized in that: In step S4, the preset universal threshold is 6-7.

8. A rolling bearing fault diagnosis system, characterized in that: The rolling bearing fault diagnosis method according to any one of claims 1 to 7, wherein the system comprises: A collection unit, used for collecting vibration data of different measuring points set for each rolling bearing in a rotating device; a processing unit, connected to the acquisition unit, for performing resonance demodulation processing on the vibration data, and then performing fast Fourier transform processing to obtain an envelope spectrum; a local normalization unit, connected to the processing unit, for performing local noise floor calculation on the envelope spectrum and performing local normalization on the spectrum peak of the envelope spectrum to obtain a normalized envelope average spectrum; A fault diagnosis unit is connected to the local normalization unit, and is used to quantify the characteristic frequency of the normalized envelope average spectrum and the amplitude of the Nth harmonic to determine the degree of prominence of the spectrum peak of the normalized envelope average spectrum, and then determine whether a fault exists, wherein N is a natural number greater than or equal to 1.

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