Predictive monitoring of asset populations in a rotating plant under operational uncertainty : a transfer learning approach
Ghauch, Ziad and Young, Andrew and Brown, Blair David and Stephen, Bruce and McArthur, Stephen and Duncan, Andrew (2025) Predictive monitoring of asset populations in a rotating plant under operational uncertainty : a transfer learning approach. Data-Centric Engineering, 6. e21. ISSN 2632-6736 (https://doi.org/10.1017/dce.2025.3)
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Abstract
Processing and extracting actionable information such as fault or anomaly indicators originating from vibration telemetry is both challenging and critical for an accurate assessment of mechanical system health and subsequent predictive maintenance. In the setting of predictive maintenance for populations of similar assets, the knowledge gained from any single asset should be leveraged to provide improved predictions across the entire population. In this paper, a novel approach to population-level health monitoring is presented adopting a transfer-learning approach. The new methodology is applied to monitor multiple rotating plant assets in a power generation scenario. The focus is on the detection of statistical anomalies as a means of identifying deviations from the typical operating regime from a time series of telemetry data. This is a challenging task because the machine is observed under different operating regimes. The proposed methodology can effectively transfer information across different assets, automatically identifying segments with common statistical characteristics and using them to enrich the training of the local supervised learning models. The proposed solution leads to a substantial reduction in mean square error relative to a baseline model.
ORCID iDs
Ghauch, Ziad, Young, Andrew



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Item type: Article ID code: 92437 Dates: DateEvent17 March 2025Published26 November 2024Accepted22 March 2024SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 24 Mar 2025 14:12 Last modified: 02 Apr 2025 00:27 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/92437