A Process Decomposition Strategy for Qualitative Fault Diagnosis of Large-Scale Processes

Most chemical processes are very large or complex. Because of this size and complexity, it is very difficult to make a diagnostic system for an entire process. Therefore, a systematic approach is required to decompose a large-scale process into subprocesses and then diagnose them. This paper suggests a method for minimization of the knowledge base and flexible diagnosis to be used in qualitative fault diagnosis based on a fault−effect tree model. The system can be decomposed for flexible diagnosis, size reduction of the knowledge base, and consistent construction of a complex knowledge base. The new node, called a gate variable, is introduced to connect the cause−effect relationships of each subprocess. For on-line diagnosis, off-line analysis is performed to construct both the fault−effect trees and the activation conditions for the gate variables. The on-line diagnosis strategy is modified to yield the same diagnosis result without system decomposition. Also, this work establishes that a plus cycle of the gate variables makes the diagnosis fail and proposes a method for resolving the problem of the plus cycle by minimizing the number of fault-propagation paths evaluated. The proposed method is illustrated with a fault diagnosis system for a large-scale boiler plant.