Combined use of sensor data and structural knowledge processed by Bayesian network: Application to a railway diagnosis aid scheme

This article describes a hybrid diagnosis system based on the combined use of sensor data (local information) and structural knowledge (global information). The approach is illustrated on an application that involves the detection of broken rail for railway infrastructure. Recently, there have been a large number of attempts to solve diagnosis problems by mixing low-level and high-level data. The inherent difficulty of combining different levels of data is offset by the benefits that accrue from additional knowledge: prior information can improve the understanding of sensor data. To introduce the application context, the paper describes first the importance of rail diagnosis. The technological solutions for broken rail detection are then listed. Since the used sensor is able to detect other kind of rail defects or singularities, the defect classification phase also involves the problem of distinguishing between real (broken rail) and false defects (rail singularities). With the help of prior information extracted from an infrastructure database, a Bayesian network is designed to infer the probabilities of membership of real or false defect classes on the basis of previous decisions. The performance of the combined use of these probabilities and sensor processing are finally presented. They demonstrate the advantage of the approach based on both a local description of the defect and a description of its environment.