基于Fisher子空间特征提取的间歇过程监控和故障诊断

1 INTRODUCTION Process monitoring and fault diagnosis are the most important tasks that determine the successful operation and the final product quality. In batch proc- ess, small changes in the operating conditions may impact the final product quality, which is often exam- ined off-line in a laboratory. If the quality variable does not satisfy a specified criterion, then it is not possible to examine the causes of fault and the time of its occurrence[1]. Therefore, early fault detection …

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed.The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or tilling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.