An Agent-Based Implementation of Hidden Markov Models for Gas Turbine Condition Monitoring

This paper considers the use of a multiagent system (MAS) incorporating hidden Markov models 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 GTs 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 an MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multiagent 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.