Publications:On monitoring heat-pumps with a group-based conformal anomaly detection approach


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Title On monitoring heat-pumps with a group-based conformal anomaly detection approach
Author Shiraz Farouq and Stefan Byttner and Mohamed-Rafik Bouguelia
Year 2018
PublicationType Conference Paper
HostPublication ICDATA' 18 : Proceedings of the 2018 International Conference on Data Science
Conference 2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA
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Abstract The ever increasing complexity of modern systems and equipment make the task of monitoring their health quite challenging. Traditional methods such as expert defined thresholds, physics based models and process history based techniques have certain drawbacks. Thresholds defined by experts require deep knowledge about the system and are often too conservative. Physics driven approaches are costly to develop and maintain. Finally, process history based models require large amount of data that may not be available at design time of a system. Moreover, the focus of these traditional approaches has been system specific. Hence, when industrial systems are deployed on a large scale, their monitoring becomes a new challenge. Under these conditions, this paper demonstrates the use of a group-based selfmonitoring approach that learns over time from similar systems subject to similar conditions. The approach is based on conformal anomaly detection coupled with an exchangeability test that uses martingales. This allows setting a threshold value based on sound theoretical justification. A hypothesis test based on this threshold is used to decide on if a system has deviated from its group. We demonstrate the feasibility of this approach through a real case study of monitoring a group of heat-pumps where it can detect a faulty hot-water switch-valve and a broken outdoor temperature sensor without previously observing these faults.