Picture of wind turbine against blue sky

Open Access research with a real impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

The Energy Systems Research Unit (ESRU) within Strathclyde's Department of Mechanical and Aerospace Engineering is producing Open Access research that can help society deploy and optimise renewable energy systems, such as wind turbine technology.

Explore wind turbine research in Strathprints

Explore all of Strathclyde's Open Access research content

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

Kenyon, Andrew and Catterson, Victoria and McArthur, Stephen and Twiddle, John (2014) An agent-based implementation of hidden Markov models for gas turbine condition monitoring. IEEE Transactions on Systems Man and Cybernetics: Systems, 44 (2). pp. 186-195. ISSN 2168-2216

[img]
Preview
PDF (Agent Based HMMs for GT monitoring)
HMM_Journal_Paper.pdf - Submitted Version

Download (431kB) | Preview

Abstract

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner.