Abstract
For process fault detection and diagnosis, a real time hybrid method based on Principle component analysis (PCA) and Bayesian belief network (BBN) is described. Upon successful identification of fault from PCA residual plot and Q statistics, information from the PCA contribution of each variable is passed to the BBN for root cause analysis. Pearl's message passing algorithm is used for belief updating. Early detection of fault, makes the methodology more reliable and robust during the process fault occurrence. The aim of this monitoring tool is to incorporate prior process knowledge along with the present observed evidence to come up with most plausible explanation of how the process is behaving. The effectiveness of the proposed method is demonstrated for a Dissolution tank model for different simulated scenarios by detecting and diagnosing the fault accurately.
Published Version
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