Anomaly Detection in Railway Tracks Using Hybrid Clustering and Spectral Analysis for Predictive Maintenance

Efficient railway track maintenance is critical for safety, operational reliability, and cost-effective asset management. While traditional inspection methods are well-established and comply with safety regulations, they face challenges in providing continuous, real-time monitoring capabilities that could enhance preventive maintenance strategies. This study introduces a hybrid machine learning framework for real-time anomaly detection in railway tracks, leveraging unsupervised clustering and spectral analysis to improve defect identification. The proposed approach integrates Principal Component Analysis (PCA) and MiniBatch K-Means clustering with a novel distance-from-mean spectral analysis technique to detect deviations in accelerometer signals from in-service locomotives. Vertical and lateral axle-box accelerations undergo Short-Term Fourier Transform (STFT) processing, generating normalized spectrograms that ensure signal consistency. Clustering identifies major anomalies based on spectral pattern shifts, while the distance-based method enhances sensitivity to subtle defects. Experimental results demonstrate that the hybrid model effectively detects 1.5–3.5% of track segments as high-risk across different railway routes. The combination of clustering and spectral deviation analysis enhances anomaly detection sensitivity, improving the identification of track defects. The framework was validated against ground truth data from Metro Valencia's track inspection records on Lines L3 and L9. Results revealed global recall scores ranging from 0.67 to 0.83, and weighted recall scores up to 0.97, depending on the method and track line. Clustering-based detection showed strong performance in identifying track width and lateral deviation issues on L3 (up to 1.00 recall), while the distance-based method excelled in detecting twist-related anomalies on L9. These findings confirm the complementary strengths of both methods in practical railway scenarios. The proposed framework complements existing inspection systems and can support enhanced maintenance planning and early defect detection. Future work will focus on expanding validation campaigns, integrating additional sensor modalities, and refining detection thresholds for broader deployment in transport operations.