Measurement point optimization strategy of model-based fault diagnosis expert system

Due to the fact that the combinatorial explosion problem of minimal diagnosis is often caused by limited measuring points, an intelligent method is proposed which can gradually reduce the minimal diagnosis by adding the system measuring points to realize fault location. First, the computing procedure is formalized by combining set enumeration tree (SE-tree) with close nodes to generate all the minimal hitting sets (i.e., minimal diagnosis). Then, by introducing the fault diagnosis synthetic information quantity and correlation matrix, and with progressive decomposition of the matrix, the measurement point optimization strategy can be achieved. Finally, using new observations and removal rules, minimal diagnosis can be gradually reduced until the only minimal diagnosis is retained. Experimental results show that the expert system model can be easily programmed and it has better efficiency, so it can meet rapidity and accuracy requirements of the complex object being diagnosed.