False alarm moderation for performance monitoring in industrial water distribution systems. (April 2022)

• Record Type: Journal Article
• Authors: Hashim, Hafiz; Clifford, Eoghan; C. Ryan, Paraic
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Performance monitoring -- False alarm moderation -- Water distribution system -- Fault detection and diagnosis -- Principal component analysis (PCA) -- Non-routine events; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101592
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: While considerable attention has been given to data driven methods that analyse and control energy systems in buildings, the same cannot be said for building water systems. As a result, approaches which support enhanced efficiency in building water consumption are somewhat underdeveloped, particularly in industrial settings. Water consumption in industrial systems features non-stationarity (i.e., variations in statistical properties over time), making it challenging to distinguish between routine and non-routine water uses. In such scenarios, fault detection and diagnosis methods that leverage multivariate statistical process control with, for example, principal component analysis and detection indices (Hotelling T 2 -statistics and Q-statistics), can be successfully used to identify system alarms. However, even with these approaches there can be a high prevalence of false alarms leading to low industry uptake of fault detection and diagnosis systems, or where in place, alarms can be ignored. To efficiently detect and diagnose water distribution system faults, false alarms should be controlled through false alarm moderation approaches so that building managers/operators only need to focus on critical system alarms or system alarms with high risk levels. This paper utilises two statistical non-parametric false alarm moderation approaches (window-based, and trial-based) that generate a second control limit for T 2 -statistics and Q-statistics. The implementation of these false alarm moderation approaches was combined with principal component analysis to detect faults with real water time series data from two case-study sites. Using both approaches false alarms were reduced, and the overall performance and reliability of the fault detection and diagnosis approach was improved. The principal component analysis model with the window-based approach was shown to be particularly effective.