HIGH-SPEED RAIL INSPECTION EXPLOITING AN ANOMALY DETECTION DATA PROCESSING APPROACH

Abstract

Rail internal defects such as detail fracture and transverse fissure are among the leading causes of track-related railway accidents. Therefore, it is critical to develop effective rail defect inspection systems and data processing methods to prevent catastrophic accidents and derailments. This study developed an anomaly detection framework using deep autoencoder (DAE) for rail defect detection. And the team evaluated its performance based on data collected by a prototype passive acoustic rail inspection system. Autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that deviate significantly from the remaining data. First, the team performed data cleaning and transfer function reconstruction using a dataset collected at the Federal Railroad Administration’s Transportation Technology Center in Pueblo, Colorado. Then, handcrafted or knowledge-driven features were extracted from the transfer functions and fed into a statistical outlier analysis as the benchmark. Also, reconstructed transfer functions at clean rail segments were directly used as the input to train and validate the DAE algorithm. The results demonstrated the effectiveness of DAE for structural discontinuity detection and showed promise for rail flaw detection.