Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures

Huo, Zhiqiang and Zhang, Yu and Francq, Pierre and Shu, Lei and Huang, Jianfeng (2017) Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access, 5 . pp. 19442-19456. ISSN 2169-3536

Full content URL: https://doi.org/10.1109/access.2017.2661967

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Incipient Fault Diagnosis of Roller Bearing using Optimized Wavelet Transform based Multi-speed Vibration Signatures.pdf

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Item Type:Article
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Abstract

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 (PSO) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modelling based Continuous Wavelet Transform (IMCWT) model. Then, a three-dimensional feature space of the statistical parameters and a Nearest Neighbor (NN) 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.

Keywords:Fault diagnosis, Vibration measurement, Continuous wavelet transforms, Roller bearing, Particle swarm optimization, Quasi-Newton minimization, Fault signatures
Subjects:H Engineering > H100 General Engineering
Divisions:College of Science > School of Engineering
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ID Code:25964
Deposited On:02 Feb 2017 09:43

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