关键词: 新能源汽车；减速器；堆叠降噪自编码网络；门控神经网络

Abstract: The reducer is a key power component in new energy vehicles. The built-in rolling bearings are prone to various failures, which can lead to major traffic accidents and seriously threaten driving safety. To address issues in diagnosing reducer bearing faults under strong background noise–such as insufficient anti-interference capability of deep models, limited diagnostic accuracy, and difficulty in tuning deep model parameters. By leveraging the synergistic effect of the strong noise-robust feature extraction capability of stacked denoising autoencoder (SDAE) and the temporal modeling advantages of Bidirectional gated recurrent unit (BiGRU), combined with bayesian optimization (BO) for model parameter tuning to enhance diagnostic performance, this paper proposes a novel fault diagnosis method for new energy vehicle reducer bearings based on the BOBiGRU-SDAE model. Experiments were conducted on common fault states of new energy vehicle reducer bearings. The results showed that the proposed method achieved a diagnostic accuracy as high as 98.75%, significantly higher than other mainstream neural network models. The study confirms that the proposed method demonstrated excellent effectiveness and accuracy. The study can provide an efficient and feasible technical solution for diagnosing new energy vehicle reducer bearing faults.

Key words: new energy vehicle; reducer; stacked denoising autoencoder; gated neural network