基于虚拟传感方法的车队疲劳监测与机动识别
Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach
AI总结：
提出基于虚拟传感的车队疲劳监测方法，仅需少量参考车辆应变片，其余用加速度计，通过散射变换和主成分分析实现疲劳损伤回归，并用公开电动自行车数据验证。
摘要：
广泛的监测成本高昂，限制了车队预测性维护策略的实施。本文提出一种基于测量的虚拟传感技术，其中仅需在少数参考车辆上安装局部应变片，而其余车队仅依赖加速度计。利用散射变换进行特征提取，主成分分析提供降维的低维数据表示。这使得能够直接从无标签使用数据中进行疲劳损伤回归。识别测量允许对降维表示进行物理解释。该方法通过一辆配备传感器的电动自行车的实验数据进行了验证，该数据已公开提供。
英文摘要：
Extensive monitoring comes at a prohibitive cost, limiting Predictive Maintenance strategies for vehicle fleets. This paper presents a measurement-based virtual sensing technique where local strain gauges are only required for few reference vehicles, while the remaining fleet relies exclusively on accelerometers. The scattering transform is used to perform feature extraction, while principal component analysis provides a reduced, low dimensional data representation. This enables direct fatigue damage regression, parameterized from unlabeled usage data. Identification measurements allow for a physical interpretation of the reduced representation. The approach is demonstrated using experimental data from a sensor equipped eBike, which is made publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
