The methodology for knowledge base compression and robust diagnosis: Application to a steam boiler plant

摘要

The new diagnosis approach, which is based on SDG to use the advantages of SDG and covers the problems that conventional SDG-based methods cannot handle, is employed for robust diagnosis. Two compression methods are suggested to prevent the drawbacks of the approach which a very large knowledge base gives in large-scale processes. The clustering of measured variables and the system decomposition enables minimization, easy construction and maintenance of the knowledge base and flexible diagnosis throughout the operational change of the process. To show the advantages of the proposed methods, the fault diagnosis system for a steam boiler plant, ENDS (ENergy Diagnosis System) was developed using the expert system shell G2. In the case study, the size of the diagnostic rules is reduced to 0.75% of that of the case without compression, and the system is verified to give fast and robust diagnosis results for the real system.