Optimal Window-Symbolic Time Series Analysis for Pattern Classification and Anomaly Detection

This article proposes the optimal window-symbolic time series analysis (OW-STSA) methodology to optimize parameters of feature extraction and pattern classification in industrial processes. The underlying theory is built upon minimization of an empirical risk function to discriminate between nominal and anomalous operations of the physical process under consideration. In particular, the proposed methodology produces: optimized windows of the time series used for pattern classification and anomaly detection, and optimized identification of feature extractors and classifier parameters. The algorithm is realized by segmenting a given time series into windows of equal size. Then, the stationary state probability vector is computed for each window in the sense of OW-STSA for anomaly prediction with locally optimal accuracy of detection performance. The proposed methodology has been experimentally validated in laboratory environment with different classifiers for two distinct industrial processes. The first experiment addresses detection of fatigue failure in polycrystalline alloy structures using time series of ultrasonic signals. The second experiment investigates detection of thermoacoustic instability in an emulated combustion system using time series of pressure-wave signals. In both experiments, the proposed OW-STSA methodology yielded excellent detection performance of anomalous behavior with multiple classification techniques.