Application of sensitive dimensionless parameters and PSO–SVM for fault classification in rotating machinery
ISSN: 0144-5154
Article publication date: 13 December 2019
Issue publication date: 30 March 2020
Abstract
Purpose
Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating machineries, this paper aims to propose a fault diagnosis method based on sensitive dimensionless parameters and particle swarm optimization (PSO)–support vector machine (SVM) for reducing the unexpected downtime and economic losses.
Design/methodology/approach
A relatively new hybrid intelligent fault classification approach is proposed by integrating multiple dimensionless parameters, the Fisher criterion and PSO–SVM. In terms of data pre-processing, a method based on wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and dimensionless parameters is proposed for the extraction of the vibration signal features. The Fisher criterion is applied to reduce the redundant dimensionless parameters and search for the sensitive dimensionless parameters. Then, PSO is adapted to optimize the penalty parameter and kernel parameter for SVM. Finally, the sensitive dimensionless parameters are classified with the optimized model.
Findings
As two different time–frequency analysis methods, a method based on a combination of WPD and EMD used to extract multiple dimensionless parameters is presented. More vital diagnosis information can be obtained from the vibration signals than by only using a single time–frequency analysis method. Besides, a fault classification approach combining the sensitive dimensionless parameters and PSO-SVM classifier is proposed. The comparative experiment results show that the proposed method has a high classification accuracy and efficiency.
Originality/value
To the best of the authors’ knowledge, very few efforts have been performed for fault classification using multiple dimensionless parameters. In this paper, eighty dimensionless parameters have been studied intensively, which provides a new strategy in fault diagnosis field.
Keywords
Acknowledgements
The authors would like to thank the Editor-in-Chief, the Associate Editor and anonymous reviewers for their valuable comments and suggestions that helped improve the quality of this manuscript. This research was partially supported by the National Natural Science Foundation of China under Grants No. 61473094 and No. 61673127. The work described in this paper was also partially supported by the Opening Foundation of the Guangdong Petrochemical Equipment Engineering and Technology Research Center (Grant No. 702/51701008), the Young Innovative Talents Program of Guangdong University (Grant No. 2018KQNCX169), the Young Innovative Talents Program of Guangdong University of Petrochemical Technology (Grant No. 2016qn17) and the Key Project of Natural Science Foundation of Guangdong (Grant No. 2018B030311054).
Conflicts of Interests: The authors declare that there is no conflict of interests regarding the publication of this article.
Citation
Qin, A., Hu, Q., Zhang, Q., Lv, Y. and Sun, G. (2020), "Application of sensitive dimensionless parameters and PSO–SVM for fault classification in rotating machinery", Assembly Automation, Vol. 40 No. 2, pp. 175-187. https://doi.org/10.1108/AA-09-2018-0125
Publisher
:Emerald Publishing Limited
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