Model-based reasoning for fault diagnosis
Recent developments in Artificial Intelligence (AI) have resulted in newer approaches using knowledge-based expert systems to problems in the design of automated process fault diagnostic systems. Despite the advantages offered by these first-generation systems over conventional methods such as fault tree analysis and signed digraphs there are some serious drawbacks. Owing to their complete reliance on heuristic or experiential knowledge, the first-generation systems are not flexible to accommodate even small changes in process configuration and are incapable of diagnosing unanticipated fault combinations. In this paper, the authors discuss a methodology that aids the development of expert systems which are process-independent, transparent in their reasoning, and capable of diagnosing a wide diversity of faults. A prototype expert system, called MODEX, has been implemented incorporating these ideas. The domain knowledge of the system is based on qualitative reasoning principles and captures physical interconnections between equipment units as well as casual relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. Using a variant of the technique adopted from fault tree synthesis, an initially observed abnormal symptom is considered to be a top level event and a tree structure is constructed as the system searches for a basic event to which the fault can be traced. The diagnostic reasoning process is driven by a problem reduction strategy. The knowledge base is process-independent, thereby enhancing the generality of the expert system. Reasoning from first-principles with the aid of causal and fault models facilitates the diagnoses of novel or unanticipated faults.
- OSTI ID:
- 5929210
- Report Number(s):
- CONF-870323-
- Country of Publication:
- United States
- Language:
- English
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