Unsupervised machine learning methodologies for identification of transversal imbalanced loads in freight railway vehicles

Abstract Imbalanced loads in freight railway vehicles pose significant risks to vehicle running safety as well as track integrity, increasing the likelihood of derailments and increasing track wear rate. This study presents a robust machine learning-based methodology designed to detect and classify transverse imbalances in freight vehicles using dynamic rail responses. The proposed approach employs wayside monitoring systems with accelerometers and strain gauges, integrating advanced feature extraction methods, including principal component analysis, log-mel spectrograms, and multi-feature-based techniques. The methodology enhances detection accuracy by normalizing features to eliminate environmental and operational variations and employing data fusion for sensitive index creation. It is capable of distinguishing between different severity levels of imbalanced loads across various wagon types. By simulating scenarios with typical European freight wagons, the study demonstrates the effectiveness of the approach, offering a valuable tool for railway infrastructure managers to mitigate risks associated with imbalanced loads. This research contributes to the field by providing a scalable, non-invasive solution for real-time monitoring and safety enhancement in freight rail operations.