Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

Machine learning for fault diagnosis of high-speed train traction systems: A review

High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.

Traction power substation balance and losses estimation in AC railways using a power transfer device through Monte Carlo analysis

The high dynamic power requirements present in modern railway transportation systems raise research challenges for an optimal operation of railway electrification. This paper presents a Monte Carlo analysis on the application of a power transfer device installed in the neutral zone and exchanging active power between two sections. The main analyzed parameters are the active power balance in the two neighbor traction power substations and the system power losses. A simulation framework is presented to comprise the desired analysis and a universe of randomly distributed scenarios are tested to evaluate the effectiveness of the power transfer device system. The results show that the density of trains and the relative branch length of a traction power substation should be considered in the evaluation phase of the best place to install a power transfer device, towards the reduction of the operational power losses, while maintaining the two substations balanced in terms of active power.

Recent advances in traction drive technology for rail transit

The traction drive system is the“heart”of rail transit vehicles.The development of sustainable,secure,economic,reliable,efficient,and comfortable contemporary rail transportation has led to increasingly stringent requirements for traction drive systems.The interest in such systems is constantly growing,supported by advancements such as permanent magnet(PM)motors,advanced electronic devices such as those using silicon carbide(SiC),new-generation insulating materials such as organic silicon,and advanced magnetic materials such as rare-earth magnets and amorphous materials.Progress has also been made in control methods,manufacturing technology,artificial intelligence(AI),and other advanced technologies.In this paper,we briefly review the state-of-the-art critical global trends in rail transit traction drive technology in recent years.Potential areas for research and the main obstacles hindering the development of the next-generation rail transit traction drive systems are also discussed.Finally,we describe some advanced traction drive technologies used in actual engineering applications.