Research on Fault Diagnosis Method for Complex Equipment Based on VPRS and NBNC

The inconsistent diagnostic information often occurs in fault diagnosis of complex equipments. In order to improve the diagnosis precision, an integrated fault diagnosis method is proposed based on variable precision rough set (VPRS) and Naive Bayesian network classifier (NBNC). Firstly, according to the relative discernibility of the original fault diagnosis decision table, the beta in VPRS is self-determined. Secondly, the beta-reducts are obtained using the heuristic reduction algorithm which is based on the VPRS condition entropy. Thirdly, the NBNC is used to analyze the reduction decision table, and consequently, the diagnostic result can be obtained. Finally, the validity and engineering practicability of the proposed method is demonstrated by an example, and the comparison results show that the proposed method combines the tolerant analysis ability of VPRS and the classification superiority of NBNC, so it is more effective than rough set theory (RST) and VPRS methods to handle the inconsistent information.