Abstract
This paper addresses the problem of fault isolation in processes which are working in different operating points. Due to nonlinearities and set-point changes, the statistical model which is obtained from data is different from one operating point to another. Therefore the classical multivariate statistical process monitoring approaches may not be suitable for monitoring and diagnosis purposes. For that, a data-driven fault isolation method is proposed which splits the process into several local models. Based on the local models, a probabilistic approach is proposed to determine the contribution of each variable to the fault detection index and find the risky variables which are responsible for the fault. Finally, the proposed method is demonstrated through its application on a laboratory setup of continuous stirred tank heater.
Published Version
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