DOI: 10.5281/zenodo.7766691
title: MetroPT2: A Benchmark dataset for predictive maintenance

description: <strong>Abstract</strong> The MetroPT2 data set is 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. Data Set Characteristics: Multivariate Time series Number of Instances: 7116940 Attribute Characteristics: Real Number of Attributes 21 Associated Tracks: Classification, Regression Missing Values N/A <strong>Data Set Information:</strong> The dataset was collected to support the development of predictive maintenance, anomaly detection, and remaining useful life (RUL) prediction models for compressors using deep learning and machine learning methods. It consists of multivariate time series data obtained from several analogue and digital sensors installed on the compressor of a train. The data span between 2022-04-28 and 2022-07-28 and includes 16 signals, such as pressures, motor current, oil temperature, flowmeter and electrical signals of air intake valves. The monitoring and logging of industrial equipment events, such as temporal behaviour and fault events, were obtained from records generated by the sensors. The data were logged at 1Hz by an onboard embedded device. You can find a schematic diagram of the air production unit of the compressor system in Figure 4 of the accompanying paper [1]. Also, the paper [2] provides a detailed examination of data collection and specifications of various types of potential failures in an air compressor system. <strong>Relevant Papers:</strong> [1]- Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). pp. 1–10. IEEE (2021) (DOI: 10.1109/DSAA53316.2021.9564181) [2] Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: The MetroPT dataset for predictive maintenance. Scientific Data 9, no. 1 (2022): 764. (DOI: 10.1038/s41597-022-01877-3) [3]-Barros, M., Veloso, B., Pereira, P.M., Ribeiro, R.P., Gama, J.: Failure detection of an air production unit in the operational context. In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, pp. 61–74. Springer (2020) (DOI: 10.1007/978-3-030-66770-2_5) <strong>Failure Information:</strong> The dataset is unlabeled, but the failure reports provided by the company are available in the following table. This allows for evaluating the effectiveness of anomaly detection, failure prediction, and RUL estimation algorithms. Nr. Start Time End Time Failure 1 2022-06-04 10:19:24.300 2022-06-04 14:22:39.188 Air Leak 2 2022-07-11 10:10:18.948 2022-07-14 10:22:08.046 Oil Leak