A hybrid clustering approach for multivariate time series – A case study applied to failure analysis in a gas turbine

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. •Real case study comprising the fault detection in a gas turbine.•Comprehensive and novel method for clustering multivariate time series.•Proposed approach allows adjusting the effects of two different data features.•Results show the ability of the method in recognizing normal and fault patterns.•Proposed approach for oversampling data using multivariate time series.