Equipment Health Assessment Based on Improved Incremental Support Vector Data Description

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 51; no. 5; pp. 3205 - 3216
• Main Authors: Zhang, Lianlian, Qiao, Fei, Wang, Junkai, Zhai, Xiaodong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive threshold; Computational efficiency; Computational modeling; Computing time; Euclidean geometry; Feature extraction; health assessment; incremental learning; Internet of Things; Maintenance; Maintenance engineering; Model accuracy; Model testing; Principal component analysis; Principal components analysis; Production; Real time; Real-time systems; Roller bearings; support vector data description (SVDD); Support vector machines
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2019.2919468

With the rapid development of Internet-of-Things and big data, health assessment of equipment is receiving more attention in recent years. It is critical to bridge the gap between real-time production data and health status evaluation, which helps maintenance team understand the health status of equipment exactly, and then make rational maintenance plans. For this purpose, this paper proposes a framework to realize real-time equipment health assessment with health status quantitatively characterized by health degree (HD). The proposed framework begins with removing redundant features using a principal component analysis (PCA) method. Then, to represent the optimal operation status, a support vector data description (SVDD) algorithm is employed for extracting normal observations in the offline part. Thereafter, HD is introduced based on the Euclidean distance between current observation and the normal sample set. In order to achieve online updating of the normal sample set, and promote accuracy and computational efficiency of the offline part, an improved incremental SVDD algorithm based on adaptive threshold <inline-formula> <tex-math notation="LaTeX">{N} </tex-math></inline-formula> (NISVDD) is proposed. A case study is used to demonstrate the effectiveness of the proposed framework and model using a benchmark dataset of rolling bearing. Results suggest that the proposed framework is effective, and PCA shows good potential to extract features and keep most of the original information. The proposed NISVDD model is able to trace the dynamics of equipment health status for whole run-to-failure process, and outperforms other models in both accuracy and computational efficiency.