Pattern recognition in multivariate time series – A case study applied to fault detection in a gas turbine

Advances in information technology, together with the evolution of systems in control, automation and instrumentation have enabled the recovery, storage and manipulation of a large amount of data from industrial plants. This development has motivated the advancement of research in fault detection, especially based on process history data. Although a large amount of work has been conducted in recent years on the diagnostics of gas turbines, few of them present the use of clustering approaches applied to multivariate time series, adopting PCA similarity factor (SPCA) in order to detect and/or prevent failures. This paper presents a comprehensive method for pattern recognition associated to fault prediction in gas turbines using time series mining techniques. Algorithms comprising appropriate similarity metrics, subsequence matching and fuzzy clustering were applied on data extracted from a Plant Information Management System (PIMS) represented by multivariate time series. A real case study comprising the fault detection in a gas turbine was investigated. The results suggest the existence of a safe way to start the turbine that can be useful to support the development of a dynamic system for monitoring and predicting the probability of failure and for decision-making at operational level. Real case study comprising the fault detection in a gas turbine.Comprehensive method for pattern recognition associated to fault prediction in gas turbines.Results show the efficiency of the proposed approach for decision-making at operational level.The whole three step method presented is flexible and portable.An extended version of the FCM algorithm suitable for the clustering of multivariate time series is applied.