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Fault diagnosis of batch processes using discriminant model. (English) Zbl 1059.93126
A two-phase technique for on-line fault diagnosis of a batch process is developed. First, the reference model is built up off-line using statistical classification: the fault data of past batches are classified into groups where each group contains a set of unsuccessful batches belonging to a particular fault. Then the current batch is referenced against the model and is classified on-line into one of the fault groups. The batch run stages of a popular polyvinyl chloride polymerization process are selected to illustrate all diagnostic steps. It is outlined that the proposed method outperforms (in terms of the rate of diagnosis success, especially at early time intervals) the known principal component analysis-based methods.

MSC:
93E10 Estimation and detection in stochastic control theory
60G35 Signal detection and filtering (aspects of stochastic processes)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62N05 Reliability and life testing
93C95 Application models in control theory
80A50 Chemistry (general) in thermodynamics and heat transfer
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