A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. (1st March 2018)

• Record Type: Journal Article
• Authors: Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu
• Is Part Of: Mechanical systems and signal processing. Volume 102(2018)
• Page Start: 278
• Page End: 297
• Publication Date: 2018-03-01
• Subjects: Intelligent fault diagnosis -- Rolling bearings -- Ensemble deep auto-encoders -- Activation functions -- Combination strategy; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.09.026
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: Different activation functions are used to design a series of auto-encoders. Ensemble deep auto-encoders are constructed for feature learning from the vibration signals. A combination strategy is designed to ensure accurate and stable diagnosis results. Abstract: Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.