Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

Abstract

As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application.

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