Research on fault diagnosis of complex equipment based on an improved disturbance search attribute reduction
Ni Zheng, Lin Zhang, Bo Zhang, Yu Zhao, Wei Si · 2016
According to the characteristics of power system fault data, a new attribute reduction method of rough set is proposed, which is used to diagnose the power system fault of complex equipment. First, the attribute reduction problem is transformed to the set covering problem and the correlation coefficient matrix is built based on the correlation matrix. Then based on the selection principle of high quality feature set, the random divergence mechanism is introduced, the disturbance searching algorithm is designed and the result of attribute reduction is obtained. At last, the UCI data set and one analog circuit are used to confirm the algorithm. The simulation results indicate that the fault diagnosis time is reduced greatly and the accurate rate is more than 91%, which showed the practicability of the algorithm.