D.: On-line Process Fault Diagnosis Using Neural Network Techniques

A neural-network based on-line fault-diagnosis system for industrial processes is presented in this paper. A multi-layer feed-forward neural network is developed and trained with symptom-fault pairs from experience of the operation of a process or from simulation analysis of that process. The trained network can be used to diagnose faults in that it can associate the abnormalities in on-line measurements with corresponding faults. Compared with diagnosis systems based on expert-systems techniques, which have several limitations such as the time consuming nature of developing the know-ledge base and the inability to cope with situations not presented in the knowledge base, the neural-network based fault-diagnosis system is easy to develop and performs robustly. The feasibility of applying such a diagnosis system to industrial processes is demonstrated by applying it to a pilot-scale mixing process and in a simulation study of a continuously-stirred tank reactor (CSTR) system. A series of experiments is carried out to investigate the performance of the neutral-network based on-line diagnosis system and it is shown that it can perform satisfactorily with partially incorrect and par-tially unavailable information. Therefore, to some extent, the system can tolerate measurement noise and model-plant mismatch