基于双流2DCNN与DCBA模块的双流2DCNN的车轮内电机故障诊断方法
Junwei Zhu1, Xupeng Ouyang1, Zongkang Jiang1
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一种新的故障诊断方法,用于使用双流2DCNN和DCBA模块的车轮内电机. 这种方法增强了特征提取,以提高电动汽车系统的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机械系统 机械系统
背景情况:
- 四轮自动驾驶系统的故障诊断对于安全性和可靠性至关重要.
- 可变的驾驶条件和恶劣的环境对现有的诊断方法构成重大挑战.
研究的目的:
- 为电动汽车的车轮内电机提出一种新的故障诊断方法.
- 在复杂的操作条件下提高诊断准确性和稳定性.
主要方法:
- 开发了一个双流2DCNN架构,集成了一个新的DCBA模块.
- 该DCBA模块从不同领域的振动信号中提取多个尺度的特征.
- 双域学习利用时间域和时间频域信号进行特征提取.
主要成果:
- 拟议的方法在各种速度,负载和路面上实现了高识别精度.
- 对比分析显示,与SENet,ECANet和CBAM等现有模型相比,性能和稳定性优越.
- 集成DCBA和双域学习有效地改善了故障特征提取.
结论:
- 新的双流2DCNN与DCBA模块为车轮内电机故障诊断提供了强大而准确的解决方案.
- 该方法显示了在现实世界电动汽车系统中应用的巨大潜力.
- 在复杂的条件下有效的特征提取是改善诊断性能的关键.
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