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Open Access research that challenges the mind...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including those from the School of Psychological Sciences & Health - but also papers by researchers based within the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

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Development of an intelligent system for detection of exhaust gas and vibration anomalies in gas turbines

Kenyon, Andrew and Catterson, Victoria and Mcarthur, Stephen and Twiddle, John (2010) Development of an intelligent system for detection of exhaust gas and vibration anomalies in gas turbines. Insight: The Journal of the British Institute of Non-Destructive Testing, 52 (8). pp. 419-423. ISSN 1354-2575

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Abstract

An unplanned outage can be costly for a utility, and gas turbines are expensive pieces of equipment to repair or replace. It is therefore vital that anomalous behaviour is flagged before damage can occur that may cause a prolonged outage. An anomaly detection system is proposed for gas turbines to monitor the related parameters and raise alarms when anomalies are identified. The proposed system incorporates machine learning algorithms based on artificial neural networks (ANN). By using ANNs trained on normal plant behaviour, it is possible to identify anomalous behaviour by the high residuals between actual and predicted outputs. Within this paper, the data mining methodology is described and the process followed before arriving at the successful approach is documented. Results from testing the approach on an industrial case study are presented and, based on these results, areas for further development are identified. It is intended to deploy the system along with several other algorithms as part of a multi-agent system for plant-wide condition monitoring. This paper will focus on the design and testing of the developed anomaly detection system.