A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection
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DOI 10.1007/s12206-020-0808-x
Journal of Mechanical Science and Technology 34 (9) 2020 Original Article DOI 10.1007/s12206-020-0808-x Keywords: · Anomaly detection · Artificial immune systems (AIS) · Equipment condition monitoring · Fault diagnosis
A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection Qinyu Jiang and Faliang Chang School of Control Science and Engineering, Shandong University, Jinan 250061, China
Correspondence to: Faliang Chang [email protected]
Citation: Jiang, Q., Chang, F. (2020). A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection. Journal of Mechanical Science and Technology 34 (9) (2020) 3565~3574. http://doi.org/10.1007/s12206-020-0808-x
Received February 25th, 2020 Revised
June 25th, 2020
Accepted July 2nd, 2020 † Recommended by Editor No-cheol Park
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 (APOAIS) 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.
1. Introduction
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Rotating machinery is one of the most common and important equipment in modern industry. And bearings are the core components and one of the most damageable core components assemblies of rotating machineries [1, 2], and high rate of failures can be contributed to bearing defects [3]. As a result, accurate and real-time fault detection for rotating machineries and bearings can guarantee safety production. In actual anomaly detection of large-scale or structural constructing complex equipments, condition information of normal operation can be collected previously, while abnormal or fault data are unknown or insufficient generally. T
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