一个快照堆叠的合并和优化方法,用于预测车辆故障
Reza Khoshkangini1,2, Mohsen Tajgardan3, Jens Lundström2
1Internet of Things and People Research Center (IoTap), Department of Computer Science and Media Technology, Malmö University, 211 19 Malmö, Sweden.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
使用传感器数据预测车辆故障对于制造商来说至关重要. 一个新的快照堆叠集团深度神经网络 (SSED) 通过分析运营历史有效地预测车辆索赔.
科学领域:
- 汽车工程 汽车工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 车辆故障给制造商带来了巨大的成本和安全问题.
- 从传感器数据中早期检测异常是预测潜在故障和保修索赔的关键.
- 复杂的预测任务需要超出简单方法的先进建模.
研究的目的:
- 为预测车辆故障开发混合优化和集体式方法.
- 建议采用快照堆叠组合深度神经网络 (SSED) 用于使用运营寿命记录进行车辆索赔预测.
主要方法:
- 数据预处理以整合,提取和细分来自各种来源的数据.
- 使用启发式优化来选择信息化车辆使用量测量的尺寸缩小.
- 结合深度神经网络的学习,将车辆使用情况映射到故障预测.
主要成果:
- 拟议的SSED方法有效地预测了使用Logged Vehicle Data (LVD) 和Warranty Claim Data (WCD) 的车辆故障情况.
- 实验结果证实了该系统在基于车辆使用历史的索赔预测方面的有效性.
- 该方法在不同的应用领域中显示出普遍性.
结论:
- 混合优化和集体深度学习方法显著改善了车辆故障预测.
- 通过车辆使用历史记录进行传感器数据分析对于准确的索赔预测至关重要.
- SSED方法为车辆的预测性维护提供了一个强大的和可通用的解决方案.
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