根据CNN-biLSTM-Attention的数据,预测汽车电缆带的老化
Kun Xia1, Qi Zhu1, Qingqing Yuan1
1Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种混合神经网络,以预测汽车电线的老化. 该模型准确地预测了降解,提高了电气化系统中的车辆安全性和性能.
科学领域:
- 汽车工程 汽车工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 现代汽车的电气化和智能化需要可靠的电线带性能.
- 由于多物理合的影响,电线束的退化对车辆的安全性和功能构成风险.
- 预测老化对于主动维护和确保运营完整性至关重要.
研究的目的:
- 开发用于汽车低压布线的先进预测模型.
- 为应对预测由于复杂的衰老因素导致的性能退化所面临的挑战.
- 提高汽车电气系统中老化水平预测的准确性.
主要方法:
- 提出了一个混合卷积神经网络 (CNN) -双向长期短期记忆 (BiLSTM) -注意力神经网络模型.
- 该模型利用了低压系统在运行期间的电压,电流和温度数据.
- 加速衰老实验从长达720小时的电线束中生成数据,用于模型训练和验证.
主要成果:
- 该CNN-BiLSTM-Attention模型证明了对电线带老化水平的有效预测性能.
- 拟议的模型实现了0.02806.6的平均绝对误差 (MAE).
- 与LSTM和随机森林模型相比,观察到32.50%和62.06%的显著错误减少.
结论:
- 混合CNN-BiLSTM-Attention模型准确地预测了电线的老化.
- 这种预测能力对于在不断发展的汽车技术中确保汽车安全和性能至关重要.
- 这项研究证实了该模型在预测退化方面优于传统方法的优势.
相关概念视频
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