Order-Information-Based Phase Partition and Fault Detection for Batch Processes

Batch processes have significantly different properties across different phases. It is essential to divide batch processes reasonably and model corresponding phases correctly for fault detection. Order information is critical for phase partition. Different from traditional phase partition methods, which customarily adopt the results of PCA for advanced research, a novel method called Markov-chain-based spectral partition (MCSP) is proposed in this paper, which takes the sequence (order) information into consideration for phase partition. Due to the combination of neighborhood information and order information, MCSP can separate each batch into major phases automatically. The time-varying characteristics remain relatively stable in each phase, which is suitable for following a Gaussian-mixture-model-based fault detection model to reflect the phase properties. Due to its simple and intuitive format, our proposed method has superior performance in fault detection. The effectiveness of MCSP is illustrated in both simulated problems and case studies of a fed-batch penicillin fermentation process.