论文标题
用于预测维护的基准数据集
A Benchmark dataset for predictive maintenance
论文作者
论文摘要
本文描述了MetroPT数据集，这是葡萄牙Porto的Urban Metro公共交通服务的可解释预测维护（XPM）项目的结果。数据是在2022年收集的，旨在评估机器学习方法的在线异常检测和故障预测。通过捕获几个类似的传感器信号（压力，温度，电流消耗），数字信号（控制信号，离散信号）和GPS信息（纬度，经度和速度），我们提供了一个可以轻松用于评估在线机器学习方法的数据集。该数据集包含一些有趣的特征，可以成为预测维护模型的良好基准。
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.
