Industrial actuator diagnosis using hierarchical fuzzy neural networks

In this paper a hierarchical structure offuzzy neural networks (FNNs) and how to train it for fault isolation given an appropriate data patterns, are presented. Fault symptoms concerning multiple simultaneous faults are harder to learn than those associated with single faults. Furthermore, the larger the set of faults, the larger the set of fault symptoms will be and, hence, the longer and less certain the training outcome. In order to overcome this problem, the proposed approach has a hierarchical structure of three levels where several FNNs are used. Thus, a large number ofpatterns are divided into many smaller subsets so that the classification can be carried out more efficiently. One ofthe advantages of this approach is that multiple faults can be detected in new data even ifthe network is trained only with datarepresenting single abrupt faults. A continuous binary distillation column having several actuated valves with PID loops has been used as testbed for the proposed approach.