Overview on Existing and Developing Methods for Track Irregularity Detection
This study firstly introduced three general methods for track irregularity detection.Then,a detailed review was conducted on the research and development of vehicle-mounted detection methods in detail from the aspects of both data-driven and model-driven methods.The main challenge of the vehicle-mounted detection method was also analyzed.Data-driven methods are widely used in the healthy monitoring of railway system,and research on dynamic detection of track irregularities and extraction of structural deformation under track is underway.As one of the model-driven methods,the inverse model method is mainly used to obtain the frequency domain characteristics of track irregularity at present.Compared with the inertial reference method,the Kalman filtering method has the advantage of integrating multiple sen-sor data to improve the detection accuracy of track irregularity.Future research should combine physical models and mechanisms with data-driven machine learning models and deep learning models to reduce the number of training sam-ples while ensuring the estimation accuracy of track irregularity.In order to ensure the comprehensive,timely and accu-rate identification of the state of the track and the structure below the track under the condition of frequent train opera-tion,it is urgent to develop dynamic detection technologies that integrate vehicle-mounted vibration,optical and acoustic sensors.Dynamic detection by fusion of data from multiple sensors and separation of different components from dynamic track irregularity are also technologies that need to be developed urgently in the future.
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