Building cognizance rule knowledge for fault diagnosis based on fuzzy rough sets

With the continuous development of huge systems, dependence on the system is continually increasing. The failure of such systems will cause huge losses. The reason for system failure is often unclear, so that inconsistency and uncertainty between fault data will appear. In the actual application process, there is a process of change. If it is possible to predict the failure probability from the monitoring parameters, it will be very beneficial to system troubleshooting. Therefore, this paper proposes a new recognition algorithm based on fuzzy rough sets, in order to adapt to the processing of uncertain fault detection data. Additionally, the optimal direction of the dynamic information entropy increment is used to predict the fault information. This can quickly find the faults and provide important information for fault detection. It is verified that the proposed algorithm can improve the early warning and the accuracy of fault diagnosis information systems in the fault simulation analysis of a diesel engine.