Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures

• Published in: IEEE access Vol. 5; pp. 19442 - 19456
• Main Authors: Zhiqiang Huo, Yu Zhang, Francq, Pierre, Lei Shu, Jianfeng Huang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bearing races; Condition monitoring; Continuous wavelet transform; Continuous wavelet transforms; Diagnostic systems; Fault diagnosis; fault signatures; Mathematical models; Optimization; Parameters; Particle swarm optimization; quasi-newton minimization; roller bearing; Roller bearings; Rolling bearings; Rotating machinery; Signatures; Three dimensional models; vibration measurement; Vibration monitoring; Vibrations; Wavelet analysis; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2661967

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e., normal bearing and three different types of defected bearings on outer race, inner race, and roller separately. Particle swarm optimization and Broyden-Fletche-Goldfarb-Shanno-based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modeling-based continuous wavelet transform model. Then, a 3-D feature space of the statistical parameters and a nearest neighbor classifier are, respectively, applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment.