Data-Driven Anomaly Detection in Smart-Railways through Self-Adaptation, Process Mining, and Digital Twins

In the era of smart transportation, ensuring the reliability, safety, and security of railway systems is paramount. This paper presents an approach to anomaly detection in smart railways, leveraging self-adaptation, process mining, and digital twins. By integrating real-time data analytics with adaptive algorithms, the framework dynamically identifies and responds to anomalies, enhancing operational efficiency and safety. Extensive data from sensors and IoT devices embedded within the railway infrastructure is continuously monitored and analyzed using advanced machine learning algorithms within digital twins. These algorithms adjust their parameters in real-time to accommodate changes in the operational environment, ensuring robustness and accuracy. Process mining techniques extract valuable insights from both historical and real-time data, identifying patterns and deviations that may indicate potential anomalies. This continuous improvement loop not only detects anomalies but also understands their underlying causes, facilitating effective interventions. Digital twins provide a virtual replica of critical assets, updated in real-time, enabling proactive maintenance and predictive analysis. They also serve as a testing ground for new algorithms and strategies, ensuring risk-free evaluation before deployment. The integration of self-adaptation, process mining, and digital twins significantly improves detection accuracy and response times. Applied to railway operational scenarios, this method effectively identifies anomalies, classifying them into minor deviations or major system faults, leading to enhanced operational efficiency, reduced downtime, and improved safety for passengers and personnel. This approach has been developed within several European projects focused on data-driven innovations for smarter railways, utilizing the latest advancements in artificial intelligence and machine learning.