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Multi-mode process monitoring based on multi-block information extraction PCA method with local neighbourhood standardisation

In order to solve the problem of multi-mode process data in complex industrial processes, a multi-mode industrial process fault monitoring method based on multi-block information extraction PCA with local neighbourhood standardisation is proposed. Firstly, define the cumulative observation information and rate of change information of the process variable. Then extract the two kinds of information from the existing observation information and obtain the data sub-blocks of the three kinds of information. Secondly, the three data sets are separately standardised by the local neighbourhood standardisation method. Accordingly, three PCA models were built based on the three information datasets obtained and each PCA model will get a monitoring result. Finally, the Bayesian inference method is used to fuse the results of the three models to obtain a final BIC monitoring index. The effectiveness and feasibility of the proposed method are proved by a numerical example and applications in Tennessee-Eastman (TE) process monitoring.