A wavelet-based clustering of multivariate time series using a Multiscale SPCA approach

•Case study comprising the fault detection in a benchmark industrial process.•Comprehensive and novel method for clustering multivariate time series.•Multiscale approach provides improvement in clustering quality.•Multiscale approach represents a useful technique in FDD problems.•Results show the ability of the method in recognizing normal and fault patterns.

Clustering and pattern recognition from data can be used as means to extract knowledge of a process which may be useful for control, predicting failures and supporting decision making, among other functions. This paper presents a method to recognize patterns in multivariate time series based on a combination of wavelet features, PCA (Principal Component Analysis) similarity metrics and fuzzy clustering. The signal analysis of some process variables is performed based on the Wavelet Transform (WT), and a Multiscale PCA Similarity factor (SPCAms) is proposed to consider the distances between objects (multivariate time series) according to a multi-resolution approach. A database extracted from the benchmark Tennessee Eastman (TE) process is used to show the efficiency of the method compared with traditional approaches in a fault detection and diagnosis problem. The clustering using SPCAms provides the recognition of a fault pattern which may be useful to support decision-making at the operational level allowing real-time monitoring of failure probability.