Active and Passive Monitoring of Rail Through the Application of Machine Learning Algorithm

Abstract

Non-destructive testing of rail is an essential part of maintaining in-service rail tracks to avoid accidents. Conventional methods such as the traditional ultrasonic technique are relatively slow and cumbersome resulting in non-frequent monitoring. This study explores active and passive techniques for continuous and long range rail damage monitoring. Firstly, the experiment, simulation and challenges of the ultrasonic guided wave generated through surface-bonded piezoelectric transducer are studied. Due to the presence of numerable inseparable modes occurring in rail, the application of machine learning algorithms is explored. Classification of damage in rail head and severity of damage have been achieved using features derived from the signal. To map changes in features with respect to damage, various ML algorithms are trained, tested and compared. Among them, the k-nearest neighbour has been found to have the highest accuracy in classifying rail head damage, while the Gaussian process regression is best suited for determining damage severity. Trained algorithms are then tested with simulated and experiment of different damage sizes. Secondly, the application of acoustic emission in rail is investigated through simulation and pencil lead break source experiments. The behaviour of rail as waveguide and wide band of generating frequency are observed to be the challenges in determining the zone of AE source. Thus, to classify the zone of AE source, a deep learning algorithm based on continuous wavelet transform is presented. This method results in 88% accuracy in finding the AE source zone. The presented study then concluded with challenges in monitoring complex geometry such as rail and application of machine learning in monitoring.