Abstract
Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.
Highlights
Due to its characteristics of comfort, speed, energy-saving and environmental protection, the high-speed railway has an increasing ratio of carrying passengers and cargo, which puts increasing requirements on the safety of the railway system
Our research group has been long engaged in the safety of railway turnout system, from turnout fault analysis [31] and simulation fault data generating [32], to fault-diagnosis method [33,34,35], based on which, this paper proposed a Bayes-based online turnout fault-diagnosis method with high accuracy, adaptability and efficiency, which can realize incremental learning and scalable fault recognition, i.e., the model can update itself and deal with new turnout fault types, making it more applicable to the fieldwork and eventually, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation
According to the definition of the local outlier factor, LOFk (p) ≈ 1 means the local density of p is similar to its k neighbors; LOFk (p) < 1 means p is in a high-density area, indicating it is a normal point; If LOFk (p) 1, the point p is far away from the normal data cluster, which means it is very likely to a novelty point
Summary
Due to its characteristics of comfort, speed, energy-saving and environmental protection, the high-speed railway has an increasing ratio of carrying passengers and cargo, which puts increasing requirements on the safety of the railway system. Our research group has been long engaged in the safety of railway turnout system, from turnout fault analysis [31] and simulation fault data generating [32], to fault-diagnosis method [33,34,35], based on which, this paper proposed a Bayes-based online turnout fault-diagnosis method with high accuracy, adaptability and efficiency, which can realize incremental learning and scalable fault recognition, i.e., the model can update itself and deal with new turnout fault types, making it more applicable to the fieldwork and eventually, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
