The training algorithm for BP network is hard to converge when the input data is large and has high dimension. Aiming at this problem,a novel fault diagnosis method based on MIV and BRBP neural networks by infrared temperature measuring is put forward. Sample data about 21 variables of circuit board under different room temperature and operating conditions are measured,and these 21 parameters are used as the initial input variables of fault diagnosis model. After MIV optimization,the reduced 12 variables will be input into BRBP neural networks to predict faults and classify the circuit board running conditions. Experiments show that the proposed neural networks model is more efficiently and more rapidly compared with the traditional BRBP neural network. The neural network model presented in the paper can effectively diagnose the circuit board faults.