Abstract
Statistical process control (SPC) has proven to be an effective tool for measuring, controlling, and improving a process through the application of statistical procedures. The most valuable SPC tool is control charts, however, if data assumptions are not met, alterations of the statistical performance of traditional control charts may be expected. In practice, the independence assumption is commonly violated in many discrete and continuous industrial processes. Recently, machine learning has been applied in the field of control chart pattern recognition (CCPR) in order to detect and identify abnormal patterns associated with assignable causes. In several instances, the performance of SVMs has been proven superior to other machine learning algorithms. In this paper, a SVM-GA-based monitoring system is presented for fault identification in AR(1) auto-correlated processes. A two stages classification scheme is presented. The initial classification stage is centered on abnormal pattern identification. The integration of a genetic algorithm in this stage boosts the performance of the SVM by simultaneously optimizing the input feature vector, the classifiers’ hyper-parameters, and the window size. A second stage provides additional information on the detected abnormal pattern in order to facilitate the fault diagnosis. The results showed a good accuracy performance on both classification stages. The proposed monitoring system yielded better classification accuracies for positive autocorrelation levels than it did for their negative counterparts. The worst classification accuracies were obtained when autocorrelation was not present.
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Cuentas, S., García, E. & Peñabaena-Niebles, R. An SVM-GA based monitoring system for pattern recognition of autocorrelated processes. Soft Comput 26, 5159–5178 (2022). https://doi.org/10.1007/s00500-022-06955-7
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DOI: https://doi.org/10.1007/s00500-022-06955-7