Predictive Maintenance of Urban Metro Vehicles: Classification of Air Production Unit Failures Using Machine Learning

Abstract

Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. Predictive maintenance (PdM) is crucial for companies to avoid unplanned outages, increase overall reliability, and lower operating costs. Failure detection and classification is a key element of predictive maintenance. In this study, a novel framework for identifying failures in the Air Production Unit (APU) of metro vehicles in real-time was proposed. The framework can also be used to create a recommendation system for predicting APU failures. To the best of our knowledge, this is the first study that detect and classify the failures in APU's metro vehicle using a real-time approach that includes machine learning. Analog sensors were found to be more significant than digital sensors in providing real-time, continuous data that is crucial for maintaining safe and efficient train operation. The proposed framework resulted in promising results with the highest F-Score of about 85% for the binary classifier and 97% for the multiclassification using the RF algorithm on the MetroPT dataset. The framework can be beneficial for metro operators by reducing maintenance costs, increasing safety, improving reliability, better managing assets, and enhancing the passenger experience. By predicting when maintenance is needed, operators can address potential safety issues before they become serious problems, improve the reliability of the metro system, and reduce disruptions for passengers. The most important analog sensor-based features include the pressure within the trains' installed air tanks, oil temperature on the compressor, and flowmeter values. The proposed framework is applicable in the field and can help operators make more informed decisions about when to repair or replace assets.