When Intelligent patrol robot inspects tour in the transmission lines, it may be affected by electromagnetic interference and fall in faults. In order to effectively complete the diagnosis of electromagnetic compatibility faults in Intelligent patrol robot, This paper proposes a fault diagnosis model based on transformed grey wolf optimizer algorithm to optimize BP neural network (TGWO-BP). Because of the complex nonlinear relationship between the symptoms of electromagnetic compatibility faults and the causes of faults, the normal BP neural network diagnosis model has a slow convergence rate, easy to fall into local extremum, and low diagnostic accuracy. In this paper, transformed grey wolf optimizer algorithm is used to optimize the weight and threshold of BP neural network, and the optimized BP neural network is applied to the electromagnetic compatibility fault diagnosis of inspection Intelligent patrol robot. The simulation results show that, compared to GWO-BP neural network and general BP neural network, the BP neural network diagnosis model optimized by transformed grey wolf optimizer algorithm has a faster convergence speed, enhanced network generalization ability and improved fault diagnosis accuracy.