Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of\nmany studies. Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural\nnetwork (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study.\nHAZOP analysis helps identify the fault modes and determine process variables monitored.The KPCA model is then constructed to\nreduce monitoring variable dimensionality.Meanwhile, the fault features of the monitoring variables are extracted, so then process\nmonitoring can be performed with the squared prediction error (SPE) statistics of KPCA. Then,multiple WNN models are designed\nthrough the use of low-dimensional sample data preprocessed by KPCA as the training and test samples to detect the fault mode\nonline. Finally, FTA approach is introduced to further locate the fault root causes of the fault mode. The proposed approach is\napplied to process monitoring and fault diagnosis in a depropanizer unit. Case study results indicate that this approach can be\napplicable to process monitoring and diagnosis in large-scale chemical plants. Accordingly, the approach can serve as an early and\nreliable basis for technicians� and operators� safety management decision-making.
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