采用小波阈值优化与改进BP神经网络的齿轮箱故障诊断方法OA

To address the issue of low fault identification accuracy and poor real-time performance of marine gearbox in high-noise operating environment,a fault diagnosis method based on wavelet threshold optimization denoising and an improved back-propagation(BP)neural network is proposed.Firstly,the wavelet threshold optimization denoising technology is used to accurately extract fault signal characteristics from the gearbox.BP neural network model is subsequently constructed for feature learning and fault classification.The results dem-onstrate that the proposed method can reach a fault identification accuracy of 98.47%.Compared to other in-telligent diagnostic methods,the proposed method not only improves the fault recognition accuracy,but also demonstrates better real-time performance.