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Fault diagnosis method for automobile generator bearing

An automobile generator and generator bearing technology, which is applied in the testing of mechanical components, electrical digital data processing, and testing of machine/structural components, etc., can solve the problems of non-stationary vibration signals, noise pollution, weak impact of bearing faults, etc. Achieve the effect of improving the signal-to-noise ratio, suppressing noise, and efficient detection

Active Publication Date: 2019-01-11
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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  • Fault diagnosis method for automobile generator bearing
  • Fault diagnosis method for automobile generator bearing
  • Fault diagnosis method for automobile generator bearing

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 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, and the residual items are removed to obtain 12 intrinsic rotation components in 4 groups. The components of the same scale are superimposed and averaged to obtain the enhanced PRC component. Calculate the autocorrelation coe...

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Abstract

The invention belongs to the field of automobile maintenance, and relates to a fault diagnosis method for an automobile generator bearing. The fault diagnosis method comprises the steps that firstly,four channels of generator bearing fault signals are collected, and improved intrinsic time-scale decomposition (IITD) is used for decomposing each channel of original signals into proper rotation components (PRC) and a monotonous trend item; the PRCs are reconstructed into one set according to the decomposing scale, autocorrelation coefficients of all the reconstructed PRCs are calculated, the maximum correlation component is selected to construct a Hankel matrix and conduct enhanced multi-resolution singular value decomposition (MRSVD), and a corresponding approximate signal and a corresponding detail signal are obtained; and finally, the optimum precise component is selected to conduct Hilbert envelope transform, and the fault type is determined. On the one hand, by utilizing improved IITD and fusing the multi-channel signals, the signal-to-noise ratio is effectively increased, and noise is inhibited; and on the other hand, the signals are further refined and purified through enhanced MRSVD, accurate fault information is obtained, and the bearing fault type is determined through Hilbert envelope demodulation.

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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IPC IPC(8): G01M13/045G06F17/50
CPCG01M13/045G06F30/20Y02T90/00
Inventor 向家伟王璐
Owner 河南富双实业有限公司
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