Research on Intelligent Fault Diagnosis Method Based on Rough Set Theory and Fuzzy Petri Nets

Rough sets theory (RST) and Fuzzy Petri nets (FPN) have been widely used in fault diagnosis. However, RST has the weakness of over-rigidity decision, and FPN has the dimensional disaster problem. In order to solve these shortcomings, according to complementary strategy, a new fault diagnosis method based on integration of RST and FPN was presented. Firstly, RST was applied to remove redundant fault features and simply fault information, so that the minimal diagnostic rules can be obtained and the fault was roughly diagnosed. Secondly, the optimal FPN structure was built and the fault diagnosis was finally realized through matrix operation of FPN. Finally, a diesel engine fault diagnosis example was analyzed, and the results show that the proposed method not only holds the ability of RST for analyzing and reducing data, but also has the advantage of FPN for parallel reasoning, so it has strong engineering practicability and validity.