基于LSTM的重型车辆虚拟负载传感器
Abdurrahman İşbitirici1,2, Laura Giarré2, Wen Xu3
1Department of Electrical, Electronic and Information Engineering, University of Bologna, 40126 Bologna, Italy.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
一个长短期记忆 (LSTM) 网络使用传感器数据估计重型车辆的质量. 这种虚拟负载传感器的准确性与商业传感器相美.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 汽车工程 汽车工程
背景情况:
- 准确估计重型车辆质量对于物流和安全至关重要.
- 传统的负载传感器可能昂贵且安装复杂.
- 开发虚拟传感技术提供了一个具有成本效益的替代方案.
研究的目的:
- 设计和评估一个虚拟负载传感器来估计重型车辆的质量.
- 使用长期短期记忆 (LSTM) 循环神经网络进行质量估计.
- 与商业传感器对比,评估拟议的基于LSTM的方法的准确性.
主要方法:
- 开发了一个双层的长短期存储器 (LSTM) 网络.
- 该LSTM网络使用车辆速度,加速度,发动机转速,扭矩和踏板位置作为输入.
- 该模型使用来自卡车制造商模拟环境的数据进行训练,并使用全球协调的轻型车辆测试周期 (WLTC) 数据进行测试.
主要成果:
- 基于LSTM的虚拟负载传感器准确估计了重型车辆的质量.
- 该估计的准确性与商业负载传感器相美.
- 该系统在4的负载质量范围内表现出可靠的性能.
结论:
- 一个长短期内存 (LSTM) 网络可以有效地作为重型车辆的虚拟负载传感器.
- 拟议的方法为车辆质量估计提供了一个有希望的,准确的,潜在的更经济的方法.
- 这项技术对智能运输系统和车队管理具有重大影响.
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