An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal

2025-08-07

AI Summary

To address the challenges of insufficient online fault prediction and limited interpretability in rail transit predictive maintenance, this paper proposes a streaming-data-driven, interpretable machine learning framework tailored for the Porto Metro system in Portugal. Methodologically, the framework integrates an incremental classification model, online extraction of dynamic time-frequency and statistical features, and a synergistic interpretability module combining natural language generation with interactive visualization—effectively mitigating class imbalance and noise interference. It represents the first approach to deliver multimodal interpretability (i.e., textual explanations and visual diagnostics) in real-time online prediction scenarios, thereby supporting actionable, human-in-the-loop operational decisions. Evaluated on a real-world metro dataset, the framework achieves an F-measure ≥ 98% and accuracy ≥ 99%, significantly reducing false alarm rates while enhancing fault detection reliability and overall system safety.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the MetroPT data set from the metro operator of Porto, Portugal. The results are above 98 % for F-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high F-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.

Problem

• Develops real-time predictive maintenance for railways using machine learning.
• Ensures high accuracy and F-measure for fault prediction in metro systems.
• Provides explainable AI for proactive maintenance decisions in transportation.

Innovation

• Real-time data-driven predictive maintenance solution
• Incremental classification with Machine Learning models
• Online fault prediction with explainability modules