Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles
Source abstract
Abstract
As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-vehicle electric-system failure-classification method, which is named Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF). Firstly, the raw data were preprocessed and dimensionality reduction was performed after the Pearson correlation coefficient; then, data features were extracted utilizing ShuffleNet and an improved DarkNet37-SE network based on DarkNet53; secondly, the inserted squeeze-and-excitation networks (SE-Net) channel attention were able to obtain more fault-related target information; finally, the prediction results of the ShuffleNet and DarkNet37-SE networks were aggregated with a fully connected neural network to output the classification results. The experimental results showed that the proposed PSDSEF-based electric vehicles electric-system fault-classification method achieved an accuracy of 97.22%, which is better than other classical convolutional neural networks with the highest accuracy of 92.19% (ResNet101); the training time is faster than the average training time of the comparative networks. The proposed PSDSEF has the advantage of high classification accuracy and small number of parameters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Advanced Battery Technologies Research · Electricity Theft Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Key Laboratory of AI and Information Processing, Education Department of Guangxi Zhuang Autonomous Region, Hechi University, Hechi 546300, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Guangxi Province (China) · Key Laboratory of AI and Information Processing (Hechi University) of Education Department of Guangxi Zhuang Autonomous Region
References (35)
Show source-provided references
1. Lu, F., Niu, R., Zhang, Z., Guo, L., and Chen, J. (2022). A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel. Appl. Sci., 12.10.3390/app12041789 ↗
2. Wang, C., Luo, J., Qing, F., Tang, Y., and Wang, Y. (2022). Analysis of the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in Taihang Mountains Based on InVEST Model. Appl. Sci., 12.10.3390/app122010662 ↗
3. Alkawsi, G., Baashar, Y., Abbas, U.D., Alkahtani, A.A., and Tiong, S.K. (2021). Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles. Appl. Sci., 11.10.3390/app11093847 ↗
4. State of Charge Estimation for Lithium-Ion Battery Based on Gaussian Process Regression with Deep Recurrent Kernel10.1016/j.ijepes.2020.106369 ↗
5. More Intelligent and Robust Estimation of Battery State-of-Charge with an Improved Regularized Extreme Learning Machine10.1016/j.engappai.2021.104407 ↗
6. Data-Driven Prediction of Battery Failure for Electric Vehicles10.1016/j.isci.2022.104172 ↗
7. An Enhanced Electro-Thermal Model for EV Battery Packs Considering Current Distribution in Parallel Branches10.1109/TPEL.2021.3102292 ↗
8. Lin, W.-H., Wang, P., Chao, K.-M., Lin, H.-C., Yang, Z.-Y., and Lai, Y.-H. (2021). Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting. Appl. Sci., 11.10.3390/app112110335 ↗
9. RS-DCNN: A Novel Distributed Convolutional-Neural-Networks Based-Approach for Big Remote-Sensing Image Classification10.1016/j.compag.2021.106014 ↗
10. Arslan, M., Kamal, K., Sheikh, M.F., Khan, M.A., Ratlamwala, T.A.H., Hussain, G., and Alkahtani, M. (2021). Tool Health Monitoring Using Airborne Acoustic Emission and Convolutional Neural Networks: A Deep Learning Approach. Appl. Sci., 11.10.3390/app11062734 ↗
11. Ahmed, S., Kamal, K., Ratlamwala, T.A.H., Mathavan, S., Hussain, G., Alkahtani, M., and Alsultan, M.B.M. (2022). Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation. Appl. Sci., 12.10.3390/app12105194 ↗
12. Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., and Kang, J.J. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors, 21.10.3390/s21082852 ↗
13. Speech Emotion Recognition Based on Convolutional Neural Network with Attention-Based Bidirectional Long Short-Term Memory Network and Multi-Task Learning10.1016/j.apacoust.2022.109178 ↗
14. Aggregated Pyramid Gating Network for Human Pose Estimation without Pre-Training10.1016/j.patcog.2023.109429 ↗
15. ChestX-Ray6: Prediction of Multiple Diseases Including COVID-19 from Chest X-Ray Images Using Convolutional Neural Network10.1016/j.eswa.2022.118576 ↗
16. Weighted Fully-Connected Regression Networks for One-Day-Ahead Hourly Photovoltaic Power Forecasting10.1016/j.apenergy.2022.120527 ↗
17. Automatic Soiling and Partial Shading Assessment on PV Modules through RGB Images Analysis10.1016/j.apenergy.2021.117964 ↗
18. Deep Convolutional Neural Networks for Semantic Segmentation of Cracks10.1002/stc.2850 ↗
19. Khare, M.R., and Havaldar, R.H. (2023). Predicting the Anterior Slippage of Vertebral Lumbar Spine Using Densenet-201. Biomed. Signal Process. Control, 86.10.1016/j.bspc.2023.105115 ↗
20. Automated Diagnosis of COVID-19 with Limited Posteroanterior Chest X-Ray Images Using Fine-Tuned Deep Neural Networks10.1007/s10489-020-01900-3 ↗
21. DS-CNN: A Pre-Trained Xception Model Based on Depth-Wise Separable Convolutional Neural Network for Finger Vein Recognition10.1016/j.eswa.2021.116288 ↗
22. Cheng, M.-Y., Sholeh, M.N., and Harsono, K. (2023). Automated Vision-Based Post-Earthquake Safety Assessment for Bridges Using STF-PointRend and EfficientNetB0. Struct. Health Monit., 147592172311687.10.1177/14759217231168709 ↗
23. A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-Source Geoinformation10.1007/s11053-021-09934-1 ↗
24. An Autonomous UAV-Assisted Distance-Aware Crowd Sensing Platform Using Deep ShuffleNet Transfer Learning10.1109/TITS.2021.3119855 ↗
25. Fault Mode Detection of a Hybrid Electric Vehicle by Using Support Vector Machine10.1016/j.egyr.2023.04.328 ↗
26. Trivedi, M., Kakkar, R., Gupta, R., Agrawal, S., Tanwar, S., Niculescu, V.-C., Raboaca, M.S., Alqahtani, F., Saad, A., and Tolba, A. (2022). Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles. Mathematics, 10.10.3390/math10193626 ↗
27. Vehicle Trajectory Prediction Method Coupled with Ego Vehicle Motion Trend Under Dual Attention Mechanism
28. Aircraft Engine Remaining Useful Life Estimation via a Double Attention-Based Data-Driven Architecture10.1016/j.ress.2022.108330 ↗
29. Radar HRRP Target Recognition Model Based on a Stacked CNN–Bi-RNN With Attention Mechanism
30. An Improved YOLOv5 Model Based on Visual Attention Mechanism: Application to Recognition of Tomato Virus Disease10.1016/j.compag.2022.106780 ↗
31. Delving Deep into Spatial Pooling for Squeeze-and-Excitation Networks10.1016/j.patcog.2021.108159 ↗
32. Android Malware Detection Based on Multi-Head Squeeze-and-Excitation Residual Network10.1016/j.eswa.2022.118705 ↗
33. Rail Wheel Tread Defect Detection Using Improved YOLOv310.1016/j.measurement.2022.111959 ↗
34. Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems10.1109/TITS.2022.3203868 ↗
35. (2023, November 14). 2022 Digital Vehicle Competition of the National Big Data Alliance of New Energy Vehicles. Available online: https://www.ncbdc.top/.