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A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection

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Abstract

Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.

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Acknowledgments

This work was supported in part by the National Key R&D Program of China under Grant 2018YFB1305300, in part by the National Natural Science Foundation of China under Grant 61673244 and Grant 61703240, and in part by the Key R&D Program of Shandong Province of China under Grant 2019JZZY010130 and Grant 2018CXGC0907. The authors also would like to thank the Case Western Reserve University Bearing Data Center for providing the bearing data for this study.

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Qinyu Jiang & Faliang Chang

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  1. Qinyu Jiang
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  2. Faliang Chang
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Correspondence to Faliang Chang.

Additional information

Qinyu Jiang received his B.S. degree from Shandong University, Weihai, China, in 2014. He is currently pursuing the Ph.D. degree with the School of Control Science and Engineering, Shandong University, Jinan, China. His research interests include fault diagnosis and classification, pattern recognition and intelligent system.

Faliang Chang received the B.S. and M.S. degrees from Shandong Polytechnic University, Jinan, China, in 1986 and 1989, respectively, and the Ph.D. degree in pattern recognition and intelligence systems from Shandong University, Jinan, in 2003. He has been a Professor of pattern recognition and machine intelligence at School of Control Science and Engineering, Shandong University since 2003. His research interests include computer vision, image processing, intelligent transportation systems, and multi-camera tracking methodology.

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Jiang, Q., Chang, F. A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection. J Mech Sci Technol 34, 3565–3574 (2020). https://doi.org/10.1007/s12206-020-0808-x

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  • DOI: https://doi.org/10.1007/s12206-020-0808-x

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