Neural network based fault diagnosis using unmeasurable inputs

Much of the earlier work presented in the area of on-line fault diagnosis focuses on knowledge based and qualitatively reasoning principles and attempts to present possible root causes and consequences in terms of various measured data. However, there are many unmeasurable operating variables in chemical processes that define the state of the system. Such variables essentially characterise the efficiency and really need to be known in order to diagnose possible malfunction and provide a basis for deciding on appropriate action to be taken by operators. This paper is concerned with developing a soft sensor to assist in on-line fault diagnosis by providing information on the critical variable that is not directly accessible. The features of dynamic trends of the process are extracted using a wavelet transform and a qualitative interpretation, and then are used as inputs in the neural network based fault diagnosis model. The procedure is illustrated by reference to a refinery fluid catalytic cracking reactor.