Real-Time Anomaly Detection in Metro Train APU Compressors: Insights from Operational Data

Abstract

Metro train systems are vital components of modern urban transportation networks. Ensuring the reliable operation of auxiliary power units (APU) is crucial for the overall performance and safety of metro trains. Anomaly detection in APU compressors can help prevent failures and minimize downtime, enhancing the efficiency and reliability of metro services. Conventional methods of anomaly detection in industrial settings often rely on rule-based systems or threshold-based alarms. While these approaches may be effective to some extent, they may not capture subtle anomalies or adapt well to evolving operating conditions. The primary challenge is to develop a system capable of continuously monitoring APU compressors and detecting anomalies in their operation. This involves analyzing operational data in real-time to identify deviations from normal behavior that may indicate impending failures or performance issues. Therefore, the Metro systems are relied upon by millions of commuters daily for efficient and timely transportation. APU compressors play a critical role in maintaining optimal conditions within train compartments. Detecting anomalies in real-time can prevent potential malfunctions or breakdowns, ensuring passenger safety and minimizing disruptions to metro services.The project, "Real-Time Anomaly Detection in Metro Train APU Compressors: Insights from Operational Data," aims to revolutionize maintenance practices in metro systems by leveraging advanced data analytics and machine learning techniques. By collecting and analyzing real-time operational data from APU compressors, this research endeavors to develop a system capable of autonomously and accurately detecting anomalies. The integration of machine learning algorithms allows for the identification of complex patterns indicative of potential issues, enabling timely interventions to prevent failures and ensure the uninterrupted operation of metro train systems. This advancement holds great promise for enhancing the safety, efficiency, and reliability of urban transportation networks.