Advances in fault diagnosis for high-speed railway: A review

The high speed railway (HSR) is a complex system with many subsystems and components. The reliability of its core subsystems is a key consideration in ensuring the safety and operation efficiency of the whole system. As the service time increases, the degradation of these subsystems and components may lead to a range of faults and deteriorate the whole system performance. To ensure the operation safety and to develop reasonable maintenance strategies, fault detection and isolation is an indispensable functionality in high speed railway systems. In this paper, the traction power supply system, bogie system, civil infrastructure system, and control and signaling system of HSR are briefly summarized, and then different fault diagnosis methods for these subsystems are comprehensively reviewed. Finally, some future research topics are discussed

Pantograph Spark Fault Detection using YOLO

Pantograph-catenary is now the dominant form of current collection for modern electric trains because they can be used for higher voltages. Faults in pantograph-catenary systems threaten the operation and safety of railway transportation. They need to be continuously monitored and controlled to maintain safe transport. Pantograph may be damaged as a result of extreme weather conditions which can affect its normal operation, leading to failure of pantograph and overhead contact line systems. Poor contact between pantograph and overhead contact line causes thermal erosion to the wire. When the pantographs are exposed to air, they could deteriorate due to electrochemical reaction with the environment since they are made of metals. Movement of catenary lines and pantograph in high crosswinds has been found to cause the wire to be trapped in the pantograph. There is a serious issue regarding the quality of images generated by pantograph video monitoring system on high-speed railway trains which often shows inconsistencies of catenary faults. The application of traditional image processing and deep learning techniques have been unable to meet the requirements of spark detection. In this paper,  a modern deep learning algorithm is proposed to detect sparks in the pantograph. Specifically, the YOLOv3 model is used to counter this problem that traditional image processing algorithms have been unable to. The results on a very large sample of data show the efficiency and real-time performance of the proposed method, which meets the requirements of pantograph spark detection in high-speed railway. Keywords: High-speed railway pantograph; Spark detection; Deep learning; YOLOv3; DOI: 10.7176/ISDE/12-3-02 Publication date:September 30th 202

Fuzzy Integral Based Multi-Sensor Fusion for Arc Detection in the Pantograph-Catenary System

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier

Particle swarm based arc detection on time series in pantograph-catenary system

Pantograph-catenary system is the most important component for transmitting the electric energy to the train. If the faults have not detected in an early stage, energy can disrupt the energy and this leads to more serious faults. The arcs occurred in the contact point is the first step of a fault. When they are detected in an early stage, catastrophic faults and accidents can be avoided. In this study, a new approach has been proposed to detect arcs in pantograph-catenary system. The proposed method applies a threshold value to each video frame and the rate of sudden glares are converted to time series. The phase space of the obtained time series is constructed and the arc event is found by using particle swarm optimization. The proposed method is analyzed by using real pantograph-videos and good result have been obtained.Pantograph-catenary system is the most important component for transmitting the electric energy to the train. If the faults have not detected in an early stage, energy can disrupt the energy and this leads to more serious faults. The arcs occurred in the contact point is the first step of a fault. When they are detected in an early stage, catastrophic faults and accidents can be avoided. In this study, a new approach has been proposed to detect arcs in pantograph-catenary system. The proposed method applies a threshold value to each video frame and the rate of sudden glares are converted to time series. The phase space of the obtained time series is constructed and the arc event is found by using particle swarm optimization. The proposed method is analyzed by using real pantograph-videos and good result have been obtained

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Non-invasive dynamic condition assessment techniques for railway pantographs

The railway industry desires to improve the dependability and longevity of railway pantographs by providing more effective maintenance. The problem addressed in this thesis is the development of an effective condition-based fault detection and diagnosis procedure capable of supporting improved on–condition maintenance actions. A laboratory-based pantograph test rig established during the course of the project at the University of Birmingham has been enhanced with additional sensors and used to develop and carry out dynamic tests that provide indicators that support practical pantograph fault detection and diagnosis. A 3D multibody simulation of a Pendolino pantograph has also been developed. Three distinct dynamic tests have been identified as useful for fault detection and diagnosis: (i) a hysteresis test; (ii) a frequency-response test; and (iii) a novel changing-gradient test. These tests were carried out on a new Pendolino pantograph, a used pantograph about to go for an overhaul, the new pantograph with individual parts replaced by old components, and on the new pantograph with various changes made to, for example, the greasing or chain tightness. Through a comparison of absolute measurements and features acquired from the three dynamic tests, it was possible to extract features associated with different failure modes. Finally, with a focus on the practical constraints of depot operations, a condition-based pantograph fault detection and diagnosis routine is proposed that draws on decision tree analysis. This novel testing procedure integrates the three dynamic tests and is able to identify and locate common failure modes on pantographs. The approach is considered to be appropriate for an application using an adapted version of the test rig in a depot setting

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Power Quality in Electrified Transportation Systems

"Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201