An intelligent diagnosis framework for roller bearing fault under speed fluctuation condition

Rotating speed fluctuation is a key problem that affects the fault diagnosis performance of mechanical equipment. Deep learning theory can use deep neural networks to realize automatic feature extraction and classification, but the existing methods always have defects in computational efficiency and diagnosis error on dealing with this problem. In this paper, combined with the advantages of deep learning, an intelligent fault diagnosis method is proposed to deal with the speed fluctuation problem. Firstly, sparse filtering is employed as a basic framework to construct the deep neural networks for feature extraction. Then, batch normalization is added to each layer to solve the frequency shift and amplitude variation properties of speed fluctuation signals. Finally, softmax regression is used as a classifier in the last layer of the deep neural networks. Two specially designed roller bearing experiments under speed fluctuation condition are adopted to verify the effectiveness of the proposed batch normalized deep sparse filtering method. The results show that the proposed method can completely ignore the influence of speed fluctuation and achieve accurate identification of different fault types, and obtain a higher accuracy than other methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A new intelligent fault diagnosis method based on deep learning is proposed in this paper, which uses sparse filtering and batch normalization techniques to address the impact of speed fluctuation on fault diagnosis. The effectiveness and superiority of the method are verified through experiments.