Enhanced sparse filtering with strong noise adaptability and its application on rotating machinery fault diagnosis

Abstract Intelligent fault diagnosis is an effective method to guarantee the continuous and efficient operation of rotating machinery. Compared with the experimental environment, noise is inevitable in real word industrial applications, which causes serious degradation of the performance of intelligent fault diagnosis methods. In view of this, this study aims to provide a method that could accurately diagnose faults under noisy environment. In this paper, we firstly discuss the characteristics of normalization and the feature extracting process of sparse filtering. Then, we propose a novel method based on the L3/2-norm, Hankel-training matrix, normalized weight matrix and feature normalization for rotating machinery fault diagnosis under noisy environment. The proposed method is applied to the fault diagnosis of rolling bearing and planetary gearbox with noise interference. The verification results confirm that the proposed method is a promising tool that shows strong noise adaptability using the training of original datasets without any time-consuming denoising preprocessing.

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