Fault diagnosis device and method for antifriction bearing based on analysis on morphological component of acoustic signal

Abstract

The invention discloses a fault diagnosis device and a method for an antifriction bearing based on the analysis on the morphological component of an acoustic signal. During the rotation process, the bearing generates an acoustic signal. Through analyzing the time domain features of the acoustic signal, a novel bearing fault diagnosis method based on the MCA and Hilbert spectrum analysis of the acoustic signal is provided. The result of the simulation and example analysis shows that, the fault acoustic signal of the bearing is formed through compounding an impulse component, a resonance component and a noise component. Through constructing a redundant dictionary composed of a coif 4 wavelet dictionary and a local cosine dictionary, the good approximation to the original signal is realized. Based on the MCA method, the generalized soft thresholding operation and the de-noising are realized. Meanwhile, the impulse component of the original signal is sparsely isolated. Finally, in combination with the impulse component of the original signal and the Hilbert frequency spectrum thereof, isolated based on the MCA method, the fault type of the antifriction bearing can be accurately identified.

Description

technical field [0001] The invention relates to a method in the field of bearing fault diagnosis, in particular to a rolling bearing fault diagnosis device and method based on acoustic signal morphological component analysis. Background technique [0002] At present, the faults of rolling bearings are mainly based on the analysis and detection of vibration signals, but this method of diagnosing faults has its limitations. Under conditions such as oil film and moving parts, it is very difficult to arrange sensors, which greatly restricts the collection of vibration signals. [0003] Mechanical noise is generated by vibration, and the mechanical acoustic signal is an extension of its vibration signal. When the mechanical vibration changes, the acoustic characteristics will also change. The experiment in the article "Rolling Bearing Fault Diagnosis Based on Acoustic Signal Wavelet Transform" shows that the acoustic signal and vibration signal under different faults can be anal...

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Problems solved by technology

But on the one hand, the signal-to-noise ratio of the actual fault acoustic signal is poor, and the composition is complex, and the impact component, resonance component and noise in the signal cannot be fully represented by any single dictionary; Absorption and other reasons, the amplitude will continue to decrease, and the periodic shock pulses caused by bearing damage are easily overwhelmed by other noises

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