Process fault diagnosis is the problem of identifying the causal origins of faults in a process, given current sensor data and a priori knowledge about the process behaviour under normal and abnormal conditions. Fault diagnosis is increasingly becoming important for process safety, improved product quality and process economics. Fault diagnosis strategies may be broadly classified as qualitative model-based, quantitative model-based and data-based approaches. The most researched approaches for fault diagnosis in the chemical engineering community have been based on data-based methods such as PCA, trend analysis and so on.

While there are several reasons for this, two of the main reasons are that the theory of observer design for nonlinear processes is not well developed and it is time-consuming to develop first principles models. With the progress that has been made in the development of advanced modeling and simulation algorithms we speculate that the latter reason might not be as important. On the other hand, the most general theory in the development of nonlinear observers is limited to models that are fault-affine. However, most realistic chemical engineering models will include fault terms that appear nonlinearly in the state equations. The proposed talk will address the development of observers that are applicable for nonlinear chemical engineering systems in which the states, inputs and faults can affect the system dynamics in a nonlinear manner. Also, although the observer is nonlinear, it only uses a linear transformation of the states, thus avoiding the need to carry out a nonlinear transformation or solving a partial differential equation, as suggested in literature [1,2,3]. Conditions for the existence of these observers and also a constructive procedure for the design of the observers will be discussed. The procedure will be validated on several examples derived from chemical engineering systems.

References

[1] Frank, P. M., 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy -- a survey and some new results. Automatica. 26(3), 459--474.

[2] Seliger, R., Frank, P., 1991. Fault-diagnosis by disturbance decoupled nonlinear observers. In: Proceedings of the 30th Conference on Decision and Control. Brighton, England, pp. 2248--2253.

[3] Persis, C. D., Isidori, A., 2001. A geometric approach to nonlinear fault detection and isolation. IEEE Transactions on Automatic Control AC-46, 853--865.