Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN

Abstract

The vibration signal of the axle box bearing of the train is affected by the track excitation and the random noise of the environment. The vibration signal is nonlinear and non-stationary, and the signal characteristics of the early fault are weak and easy to be submerged, which leads to the low accuracy of the weak fault diagnosis of the bearing. To solve this problem, a weak fault diagnosis method for train axle box bearing based on parameter optimization Variational Mode Decomposition (VMD) and improved Deep Belief Network (DBN) is proposed. Firstly, the nonlinear convergence factor, Levy flight theory and greedy algorithm optimization theory are introduced into the Grey Wolf optimization algorithm (GWO), and an improved GWO algorithm based on hybrid strategy is proposed to improve the performance of the algorithm and solve the local optimal problem of the algorithm. Secondly, the improved GWO is applied to optimize the VMD parameters, which is used for signal decomposition. And the fault feature information of modal components with maximum correlation coefficient is extracted by multi-scale scatter entropy. Finally, the improved GWO algorithm is applied to optimize the parameters of the DBN to solve the parameter setting problem, and the optimized DBN is used as a pattern recognition algorithm for weak fault diagnosis of bearings. Through experimental comparison and analysis, the proposed method can effectively solve the problem of weak fault diagnosis of axle box bearings, and has high diagnostic accuracy.