基于 BAS-BP 模型 HMCVT 换段液压系统故障的诊断方法.

[Objectives]In order to improve the stability and safety of the hydraulic system of the hydro-mechanical continuously variable transmission (HMCVT), a diagnosis method to deal with the hydraulic fault of the hydraulic system was designed. [Methods]Using the independently developed hydro mechanical continuously variable transmission test platform, five oil circuit fault state mode data sets were obtained. 120 sets of single fault samples and 21 sets of combined fault samples were obtained by data preprocessing and random sampling. Based on the beetle antenna search (BAS) lgorithm and BP (back propagation) neural network, a fault diagnosis model was established for 120 sets of single fault samples after processing. The standard BP neural network model and the optimized BP neural network model were tested and compared. [Results]The BAS-BP method used in this paper realized the classification of five oil circuit state patterns for the test samples. Compared with standard BP neural network, BAS-BP method could better prevent the network from being limited by local minimum, and the accuracy of fault diagnosis was improved by 10%. [Conclusions]Compared with the conventional optimization algorithm, BAS-BP method required shorter training time and faster convergence speed. The running speed of the algorithm increased by 85.76%,and it had better stability and discrimination accuracy. In particular, it should be pointed out that this method was still effective for the identification of combined faults. [ABSTRACT FROM AUTHOR]