The challenges related to current energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive market of the Oil & Gas sector.

Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips is a key factor to predict their occurrence and avoid further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required.

Clustering is a data mining technique that addresses the scope of partitioning multi-variate time series into a given number of homogeneous and separated groups. In such a manner, the multi-variate time series belonging to the same cluster are very similar to each other and dissimilar to those of the other clusters.

This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study, by investigating transients acquired from a fleet of Siemens gas turbines in operation during three years.

The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90 % in almost all cases.

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