Early wheel flat detection: an automatic data-driven wavelet-based approach for railways

Wheel defects can induce damage to the railway tracks, increasing considerable maintenance costs for both railway administrations and rolling stock operators. This paper aims to develop an unsupervised early damage detection methodology, capable of automatically distinguishing a defective wheel from a healthy one, with respect to the small flat size. The proposed methodology is based on the acceleration and shear time histories evaluated on the rails for the passage of traffic loads, and involves the following steps: (i) data acquisition from sensors; (ii) feature extraction from acquired responses with continuous wavelet transform (CWT) model; (iii) feature normalisation to suppress environmental and operational variations; (iv) data fusion to merge the features from each sensor and enhance sensitivity to detect wheel defects; and (v) feature classification to classify the extracted features into two categories: a healthy wheel or a defective one. The shear and acceleration measurement points are strategically defined in order to examine the sensitivity of the proposed methodology, not only to the type of sensors, but also to the position where they are installed. It has been demonstrated that one sensor can detect a defective wheel automatically, allowing the development of an easy-to-implement low-cost monitoring system.

Automated summary

This paper aims to develop an unsupervised early damage detection methodology that can automatically distinguish a defective wheel from a healthy one by evaluating the acceleration and shear forces on railway tracks. The proposed methodology involves data acquisition, feature extraction, feature normalization, data fusion, and feature classification, and has been shown to effectively detect wheel defects.