Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Lu, Siliang; Wang, Xiaoxian; Liu, Fang; He, Qingbo; Liu, Yongbin; Zhao, Jiwen

doi:10.1155/2016/8139273

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Fast kurtogram (FK) was originally proposed by Antoni et al [9] to automatically determine the optimal filter center and bandwidth of the bandpass filter, overcoming the shortcomings of manually determining the parameters of the bandpass filter. In recent years, increasing attentions have been paid to the advantages of using FK in the fault diagnosis of rotating machinery [10][11][12][13]. However, the FK reveals weaknesses when there are strong random noise and interfering frequencies in the original vibration signals.…”

Section: Introductionmentioning

confidence: 99%

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

et al. 2020

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Many methods have been developed for machinery fault diagnosis addressing only Gaussian noise reduction, the major weaknesses of these methods are their performance for non-Gaussian noise suppression. Modulation signal bispectrum (MSB) is a useful signal processing method with significant advantages over traditional spectral analysis as it has the unique properties of Gaussian noise elimination and demodulation. However, it is susceptible to non-Gaussian noise that normally occurs in the practical applications. In view of the deficiency of MSB, in this paper, an autoregressive (AR) modeling filter was developed based on non-Gaussian noise suppression to improve the performance of MSB for machinery fault detection. The AR model was considered as a pre-filter process unit to reduce the non-Gaussian noise. And the order of the AR model, which can affect the performance of the AR filter, was determined adaptively using a kurtosis-based indicator. However, the existing nonlinear modulation components remain in the AR filtered signal. The MSB was then applied to decompose the modulated components and eliminate the Gaussian noise from the filtered signal using AR model for the fault feature extraction accurately. The advantage of the AR model can effectively manage non-Gaussian noise, whereas the MSB can suppress Gaussian noise and is illustrated in the simulation signal. Furthermore, the proposed AR-MSB method was applied to analyze the vibration signals of defective bearings with outer race and inner race faults. By comparison, the proposed approach had a superior performance over conventional MSB and fast kurtogram in extracting fault features and was precise and effective for rolling element bearing fault diagnosis.

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