Abstract :
An integrated approach using neural networks for detecting and diagnosing process failures is presented. The system, which consists of three major components, quantitative networks, qualitative networks, and inverse qualitative networks, effectively reduces the inherent ambiguity of forward-mapping neural networks by incorporating the inverse mapping neural networks, which corresponds to the mapping from the fault space to the symptom space, and identifies the most plausible case in a process. The system is tested on four kinds of possible fault groups, including novel single faults, two two-fault groups, and sensor faults. It is shown that, due to the successful integration of quantitative information and qualitative information associated with process data, the system can successfully and substantially improve the diagnostic performance without additional information
