In order to improve the feature extraction ability of the fault signal of diesel engine water pump cover and diagnose the fault type quickly and effectively, a fault diagnosis method combining robust local mean decomposition algorithm (RLMD) with BP neural network optimized by BAS algorithm is proposed. Firstly, the collected signal sequence is denoised by wavelet threshold and RLMD, and then the signal components (PF) with high similarity with the original signal are screened out according to the Spearman correlation coefficient. Then, the wavelet energy entropy and wavelet singular value entropy of each component are calculated as fault features. Finally, the BP neural network optimized by BAS is used for fault diagnosis and fault pattern recognition. At the same time, compared with neural networks optimized by GA-BP and PSO-BP. The results show that BAS-BP is superior to PSO-BP and GA-BP neural network in all aspects, and the fault classification accuracy of BAS-BP can reach 98.90%.