A hybrid multiple sensor fault detection, diagnosis and reconstruction algorithm for chiller plants

Fong, K. F.; Lee, C. K.; Leung, M. K. H.; Sun, Y. J.; Zhu, Guangya; Baek, Seung Hyo; Luo, X. J.; Lo, Tim Ka Kui; Leung, Hetty Sin Ying

Abstract

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

• Journal Article Type: Article
• Acceptance Date: Feb 24, 2023
• Online Publication Date: Mar 22, 2023
• Publication Date: Sep 3, 2023
• Deposit Date: Apr 27, 2023
• Publicly Available Date: Mar 23, 2024
• Journal: Journal of Building Performance Simulation
• Print ISSN: 1940-1493
• Electronic ISSN: 1940-1507
• Publisher: Taylor & Francis (Routledge)
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 5
• Pages: 588-608
• DOI: https://doi.org/10.1080/19401493.2023.2189303
• Keywords: Fault detection and diagnosis; big data analytics; machine learning; pattern recognition; chiller plant; sensor faults
• Public URL: https://uwe-repository.worktribe.com/output/10594748
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/19401493.2023.2189303