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A Fault Diagnosis Method for Automobile Generator Bearings

A technology for automotive generators and generator bearings, which is applied in the testing of computer components, mechanical components, and machine/structural components. Improve the signal-to-noise ratio, efficiently detect, and suppress noise

Active Publication Date: 2021-01-26
河南富双实业有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When a generator bearing fails, especially a composite fault, its vibration signal is non-stationary and has very complex characteristics, and is polluted by noise in the environment
The impact of bearing faults is very weak, and it is difficult to extract useful information from complex fault signals

Method used

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  • A Fault Diagnosis Method for Automobile Generator Bearings
  • A Fault Diagnosis Method for Automobile Generator Bearings
  • A Fault Diagnosis Method for Automobile Generator Bearings

Examples

Experimental program
Comparison scheme
Effect test

Embodiment example 1

[0077] Implementation Case 1: Fault Diagnosis of Outer Race of Automobile Generator Bearing

[0078] The model of the faulty bearing is 6205, and the basic information is as follows: the number of rolling elements is n=9, the diameter of the rolling elements is d=7.94mm, and the pitch diameter of the bearing is D=39.0398mm. The test parameters are as follows: bearing no-load running speed Rev = 1800r / min, sampling frequency f s = 12kHz. According to the calculation formula of the fault characteristic frequency of the outer ring of the bearing:

[0079]

[0080] In formula (10), α represents the bearing contact angle, and the calculated characteristic frequency of the outer ring fault is 107.5 Hz.

[0081] The time-domain waveform and the Hilbert envelope spectrogram of the original signal in the outer circle are as follows: figure 2 As shown, it can be seen that the spectrum lines of the envelope spectrum are messy, and the fault type cannot be determined. The IITD dec...

Embodiment example 2

[0082] Implementation Case 2: Bearing Inner Ring Fault Diagnosis

[0083] Take the above-mentioned bearing inner ring fault signal, bearing no-load running speed Rev=1800r / min, sampling frequency f s =12kHz, the formula for calculating the fault characteristic frequency of the inner ring of the bearing is:

[0084]

[0085]By calculation, the characteristic frequency of the inner ring fault is 162.5Hz.

[0086] The time-domain waveform and Hilbert envelope spectrogram of the original signal in the inner circle are as follows: Figure 5 As shown, due to noise interference, no matter in time domain or frequency domain, the fault type cannot be judged. The IITD decomposition is performed on the 4-channel original signal, the number of decomposition layers is 4 layers, and the residual items are removed to obtain 4 groups of 12 intrinsic rotation components. The components of the same scale are superimposed and averaged to obtain the enhanced PRC component. Calculate the au...

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Abstract

The invention belongs to the field of automobile maintenance and relates to a fault diagnosis method for automobile generator bearings. First, the 4-channel generator bearing fault signal is collected, and the original signal of each channel is decomposed into an intrinsic rotation component and a monotonic trend term using an improved intrinsic time scale decomposition. The intrinsic rotation components are reconstructed into a group according to the decomposition scale, the autocorrelation coefficients of each reconstructed intrinsic rotation components are calculated, the maximum correlation component is selected to construct the Hankel matrix, and the enhanced multi-resolution singular value decomposition is performed to obtain the corresponding approximate signal and detail signal. Finally, the optimal detail component is selected for Hilbert envelope transformation to determine the fault type. On the one hand, the improved inherent time scale is used to decompose and fuse multi-channel signals to effectively improve the signal-to-noise ratio and suppress noise; on the other hand, the enhanced multi-resolution singular value decomposition further refines and purifies the signal to obtain accurate fault information, and through Hilbert envelope demodulation determines bearing fault type.

Description

technical field [0001] The invention belongs to the field of automobile maintenance, and in particular relates to a fault diagnosis method for an automobile generator bearing. Background technique [0002] The car alternator is an important part of the car's ignition system, and its function is to provide the main power for the car. When the engine is running normally, the generator supplies power to all electrical equipment except the starter and charges the battery at the same time. The quality of the generator bearing directly affects the function of the generator and is related to the operation status of the whole vehicle. Therefore, it is very important to find and diagnose generator bearing faults in time. When a generator bearing fails, especially a composite fault, its vibration signal is non-stationary and has very complex characteristics, and is polluted by noise in the environment. The impact of bearing faults is very weak, and it is difficult to extract useful...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045G06K9/00G06K9/62
CPCG01M13/045G06F30/20Y02T90/00
Inventor 向家伟王璐
Owner 河南富双实业有限公司