A Multitask Learning Method for Rail Corrugation Detection Using In-Vehicle Responses and Noise Data

Rail corrugation is a type of track geometry irregularity that induces sharp noise and reduces the service life of track components. The traditional manual inspection method of corrugation detection requires high labor costs. In general, the rail corrugation status of subway lines can be divided into three types according to wavelength: short wave, medium wave and healthy. Corrugation with different degrees of deterioration has different values of wave depth. To achieve more efficient and intelligent inspection, this study proposes a multitask learning method to detect metro rail corrugation by using in-vehicle responses and noise data. To this end, we develop a portable vehicle-mounted device to collect car body acceleration and vehicle interior noise. Second, we propose a data fusion method based on a 1/3 octave general vibration level of acceleration and noise data. Third, we develop a multitask learning model based on convolution and attention techniques that have two task towers: corrugation wavelength classification and wave depth regression. Finally, we apply the real-world data to the proposed framework. The results show that compared with single-task learning, the proposed multitask learning has a better performance in corrugation detection tasks. The
$F_{1}$
score of wavelength classification increased from 0.762 to 0.971, an increase of 27%; the Mean Absolute Percentage Error of wave depth regression decreased from 12% to 4.96%, a decrease of 59%. In addition, the training time consumption can be reduced by up to 66%.