A Machine Learning-based Algorithm for Automated Detection of Frequency-based Events in Recorded Time Series of Sensor Data
基于机器学习的传感器时间序列频率事件自动检测算法AI 解读 ↓
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基于机器学习的传感器时间序列频率事件自动检测算法
- 研究问题
- 研究车辆测试中的传感器时间序列，如何自动检测具有频率特征的事件，并确定人工目视检查难以准确划定的时间边界。
- 方法与思路
- 作者将序列转换为时频尺度图，经过滤波突出相关信号，训练目标检测模型识别事件，再将图中的事件映射回时间区间。
- 材料陈述的贡献
- 摘要报告算法在未见数据集上的事件检测精确率为 0.97，并称能给出清晰的时间边界，帮助标准化车辆测试数据分析。
- 阅读关注点
- 应核对事件标签和时间边界的定义、训练测试划分、精确率计算方式，以及召回率、边界误差和跨场景泛化表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明样本规模、召回率及指标不确定性，不能据精确率判断整体效果。
原文摘要
Automated event detection has emerged as one of the fundamental practices to monitor the behavior of technical systems by means of sensor data. In the automotive industry, these methods are in high demand for tracing events in time series data. For assessing the active vehicle safety systems, a diverse range of driving scenarios is conducted. These scenarios involve the recording of the vehicle's behavior using external sensors, enabling the evaluation of operational performance. In such setting, automated detection methods not only accelerate but also standardize and objectify the evaluation by avoiding subjective, human-based appraisals in the data inspection. This work proposes a novel event detection method that allows to identify frequency-based events in time series data. To this aim, the time series data is mapped to representations in the time-frequency domain, known as scalograms. After filtering scalograms to enhance relevant parts of the signal, an object detection model is trained to detect the desired event objects in the scalograms. For the analysis of unseen time series data, events can be detected in their scalograms with the trained object detection model and are thereafter mapped back to the time series data to mark the corresponding time interval. The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97 in event detection, providing sharp time interval boundaries whose accurate indication by human visual inspection is challenging. Incorporating this method into the vehicle development process enhances the accuracy and reliability of event detection, which holds major importance for rapid testing analysis.