Industrial actuator diagnosis using hierarchical fuzzy neural networks

In this paper a hierarchical structure of fuzzy 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 of patterns are divided into many smaller subsets so that the classification can be carried out more efficiently. One of the advantages of this approach is that multiple faults can be detected in new data even if the network is trained only with data representing single abrupt faults. A continuous binary distillation column having several actuated valves with PID loops has been used as test bed for the proposed approach.