Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 9; p. 2097
• Main Authors: Wang, Yanxue, Liu, Fang, Zhu, Aihua
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06-05-2019; MDPI
• Subjects: Accuracy; Algorithms; Bearings; Clinical decision making; Decision making; Dempster-Shafer evidence theory; Diagnostic systems; Energy spectra; Fault diagnosis; fuzzy preference relations; Fuzzy sets; hybrid classifier ensemble; Industrial equipment; Nuclear power plants; Pattern recognition; Roller bearings; rolling element bearing; Support vector machines; Wavelet analysis; Beijing China; China; hybrid classifier ensemble; rolling element bearing; Dempster-Shafer evidence theory; fuzzy preference relations
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19092097

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory.