Abstract: Multiscale classification has potential advantages for monitoring industrial processes generally driven by events in different time and frequency domains. In this study, we adopt stationary wavelet transform for multiscale analysis and propose an applicable scale selection method to obtain the most discriminative scale features. Then using the multiscale features, we construct two classifiers: (1) a supported vector machine (SVM) classifier based on classification distance, and (2) a Bayes classifier based on probability estimation. For the SVM classifier, we use 4-fold cross-validation and grid-search to obtain the optimal parameters. For the Bayes classifier, we introduce dimension reduction techniques including kernel Fisher discriminant analysis (KFDA) and principal component analysis (PCA) to investigate their influence on classification accuracy. We tested the classifiers with two simulated benchmark processes: the continuous stirred tank reactor (CSTR) process and the Tennessee Eastman (TE) process. We also tested them on a real polypropylene production process. The performance comparison among the classifiers in different scales and scale combinations showed that when datasets present typical scale features, the multiscale classifier had higher classification accuracy than conventional single scale classifiers. We also found that dimension reduction can generally contribute to a better classification in our tests.

Key words: Multiscale analysis, Stationary wavelet transform, Multi-class classifier, Feature extraction, Process monitoring

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