Predictive monitoring of asset populations in a rotating plant under operational uncertainty: a transfer learning approach
Author(s)
Type
Journal Article
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.
Date Issued
2025-03-17
Date Acceptance
2024-11-26
Citation
Data-Centric Engineering, 2025, 6
ISSN
2632-6736
Publisher
Cambridge University Press
Journal / Book Title
Data-Centric Engineering
Volume
6
Copyright Statement
© The Author(s), 2025. Published by Cambridge University Press This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
License URL
Subjects
change point detection
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
condition monitoring
domain adaptation
Engineering
Engineering, Multidisciplinary
power station
rotating plant
Science & Technology
statistical discrepancies
Technology
transfer learning
vibration
Publication Status
Published
Article Number
e21
Date Publish Online
2025-03-17