水准点(测量)
全球定位系统
计算机科学
异常检测
预测性维护
数据挖掘
预测建模
数据集
机器学习
人工智能
可靠性工程
工程类
地理
地图学
电信
作者
Bruno Veloso,Rita P. Ribeiro,João Gama,P. Pereira
标识
DOI:10.1038/s41597-022-01877-3
摘要
The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.
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