Transfer-Based Deep Neural Network for Fault Diagnosis of New Energy Vehicles

Abstract

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New energy vehicles are crucial for low carbon applications of renewable energy and energy storage, while effective fault diagnostics of their rolling bearings is vital to ensure the vehicle’s safe and effective operations. To achieve satisfactory rolling bearing fault diagnosis of the new energy vehicle, a transfer-based deep neural network (DNN-TL) is proposed in this study by combining the benefits of both deep learning (DL) and transfer learning (TL). Specifically, by first constructing the convolutional neural networks (CNNs) and long short-term memory (LSTM) to preprocess vibration signals of new energy vehicles, the fault-related preliminary features could be extracted efficiently. Then, a grid search method called step heapsort is designed to optimize the hyperparameters of the constructed model. Afterward, both feature-based and model-based TLs are developed for the fault condition classifications transfer. Illustrative results show that the proposed DNN-TL method is able to recognize different faults accurately and robustly. Besides, the training time is significantly reduced to only 18s, while the accuracy is still over 95%. Due to the data-driven nature, the proposed DNN-TL could be applied to diagnose faults of new energy vehicles, further benefitting low carbon energy applications.

Keywords

• low carbon energy applications
• deep learning
• transfer learning
• fault diagnosis
• energy vehicle