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• W2756414053 abstract "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." @default.
• W2756414053 created "2017-09-25" @default.
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• W2756414053 date "2017-11-01" @default.
• W2756414053 modified "2023-09-30" @default.
• W2756414053 title "A hybrid clustering approach for multivariate time series – A case study applied to failure analysis in a gas turbine" @default.
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• W2756414053 doi "https://doi.org/10.1016/j.isatra.2017.09.004" @default.
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