Rail Defect Detection Using Fiber Optic Sensors and Wavelet Algorithms

Abstract

Early detection of rail defects can avoid derailments and costly damage to the train and railway infrastructure. Small breaks, cracks or corrugations on the rail can quickly propagate after only a few train cars have passed over it, creating a potential derailment. The current technology makes use of a dedicated instrumented car or a separate railway monitoring vehicle to detect large breaks. These cars are usually equipped with accelerometers mounted on the axle or side frame. The simple detection algorithms use acceleration thresholds which are set at high values to eliminate false positives. As a result, rail surface defects that produce low amplitude acceleration signatures may not be detected, and special track components that produce high amplitude acceleration signatures may be flagged as defects. This paper presents the results of a feasibility study conducted to develop new and more advanced sensory systems as well as signal processing algorithms capable of detecting various rail surface irregularities. A dynamic wheel-rail interaction model was used to simulate train dynamics as a result of rail defects and to assess the potential of this new technology on rail defect detection. In a future paper, we will present experimental data in support of the proposed model and simulations.