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

Yuping Wang, Weidong Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
74 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number796528
Number of pages22
JournalFrontiers in Energy Research
Volume9
DOIs
Publication statusPublished - 14 Dec 2021

Bibliographical note

Copyright © 2021 Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funder

This research was supported by the National Natural Science Foundation of China (Project No. 51975444) and an advanced manufacturing lab establishment funding supported by the Wuhan University of Technology.

Keywords

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

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Economics and Econometrics

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