Improved railway vehicle inspection and monitoring through the integration of multiple monitoring technologies

The effectiveness and efficiency of railway vehicle condition monitoring is increasingly critical to railway operations as it directly affects safety, reliability, maintenance efficiency, and overall system performance. Although there are a vast number of railway vehicle condition monitoring technologies, wayside systems are becoming increasingly popular because of the reduced cost of a single monitoring point, and because they do not interfere with the existing railway line. Acoustic sensing and visual imaging are two wayside monitoring technologies that can be applied to monitor the condition of vehicle components such as roller bearing, gearboxes, couplers, and pantographs, etc. The central hypothesis of this thesis is that it is possible to integrate acoustic sensing and visual imaging technologies to achieve enhancement in condition monitoring of railway vehicles. So this thesis presents improvements in railway vehicle condition monitoring through the integration of acoustic sensing and visual imaging technologies

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

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

Proceedings of COMADEM 201

Railway Polygonized Wheel Detection Based on Numerical Time-Frequency Analysis of Axle-Box Acceleration

The increasing need for repairs of polygonized wheels on high-speed railways in China is becoming problematic. At high speeds, polygonized wheels cause abnormal vibrations at the wheel-rail interface that can be detected via axle-box accelerations. To investigate the quantitative relationship between axle-box acceleration and wheel polygonization in both the time and frequency domains and under high-speed conditions, a dynamics model was developed to simulate the vehicle-track coupling system and that considers both wheel and track flexibility. The calculated axle-box accelerations were analyzed by using the improved ensemble empirical mode decomposition and Wigner-Ville distribution time-frequency method. The numerical results show that the maximum axle-box accelerations and their frequencies are quantitatively related to the harmonic order and out-of-roundness amplitude of polygonized wheels. In addition, measuring the axle-box acceleration enables both the detection of wheel polygonization and the identification of the degree of damage. Document type: Articl

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Advanced signal processing of wayside condition monitoring of railway wheelsets

Railway transport is an efficient and environmentally benign method of transport. With global warming effects intensifying it has become more urgent that mobility and economic prosperity are maintained by delivering increased transport efficient. Hence, railway transport has a significant role to play in the forthcoming decades. Punctuality and safety of railway operations is critical in ensuring unhindered transportation for passengers and freight. Rolling stock are required to operate at higher speeds and carry heavier axle loads than ever before. This puts increased pressure to rolling stock operators and infrastructure managers in trying to avoid disruption and potential accidents which also leads to higher transportation costs. Remote condition monitoring has increased in significance for railway transport over the last few years. However, there are still a lot to be done before breakthrough remote condition monitoring technologies are delivered at commercial scale in the wider international railway network. Different remote condition monitoring systems are installed wayside in order to evaluate the structural integrity of rolling stock wheelsets, detect any potential rolling stock fault in time and minimize the likelihood of a serious railway accident. The existing wayside condition monitoring system are based on infrared cameras, acoustic arrays and strain gauges. Despite significant investments by the rail industry in this area, false alarms can still occur and many of condition monitoring systems are able to detect faults once they become critical. In the present thesis, a novel approach based on integration of acoustic emission and vibration analysis together with advanced signal progressing is detailed. Tests ranging from laboratory tests under controlled conditions, all the way up to trials under actual operational conditions in the UK network have been carried out, yielding promising results. The experimental methodology employed has shown that acoustic emission is particularly efficient in detecting and ranking potential axle bearing defects. When acoustic emission is coupled with vibration analysis, it is possible to detect axle bearing defects whilst avoiding misinterpretation of wheel flats for axle bearing defects. The results obtained suggest that the widespread use of the reported methodology in the railway is feasible. The novel RCM system can enhance the reliability, availability, maintainability and safety of rolling stock wheelsets. Experimental work have been carried out under actual operational conditions in UK rail network at Cropredy, at Chiltern Railway line. The novel RCM system has been installed adjacent to Hot Box Axle Detector for comparison purposes. No interference on the track circuits is the main advantage of the proposed system. During the signal processing module of the system, freight and passenger train waveforms were identified to contain evidence of potential bearing faults. The results still require follow up validation from Network Rail. Time, frequency and time-frequency analysis have been applied to the acquired data. High amplitude peaks and signal modulation were visible at raw data. The acquired signals were transferred to frequency domain. Harmonics in frequency distribution were clearly seen. These frequency bands can be used as a reference for the band pass filter at HFRT process. HFRT algorithm has been effectively applied in the captured data in order to identify the fundamental fault frequency and its harmonics. Sidebands were also visible. TSK analysis was also applied in the raw signals. Frequency bands with high kurtosis values can be used as a reference for further analysis. In addition, laboratory experiments at University of Birmingham and Long Marston trials under controlled conditions have been carried out in order to evaluate the reliability of the system in early diagnosis of wheel and axle bearing defects. Acoustic emission and vibration signals have been collected. From the results obtained, it has effectively demonstrated that fault detection can be achieved using the frequency distribution of the signal. Defect type evaluation can be carried out by detecting the fundamental fault frequency at the HFRT process and fault quantification can achieved by Normalized Moving RMS analysis. In summary, the research contribution of this work is presented below: $\bullet$ Development and assessment of the vibroacoustic condition monitoring system for railway wheelsets. Experimental methodology and results considered in this study are the main contributions in the literature of this field. $\bullet$ In service passenger and freight trains have been monitored. Detection of potential bearing faults has been achieved. $\bullet$ Novel methodology applied at the acquired in-service data in order to determine the appropriate frequency range for the band-pass filter during the HFRT process. Frequency bands with high kurtosis values can be used as a reference for the band-pass filter. In addition, harmonics have been presented in frequency distribution of the signal. The frequency bands that harmonics were appeared can also be used for the design of the band-pass filter. $\bullet$ Comparison between advanced signal processing techniques using laboratory, in-field and in in-service signals. Detection of wheelsets faults, identification of type of the defect and quantification of fault severity can be achieved when combination of algorithm is applied at raw signals

An extensive review of vibration modelling of rolling element bearings with localised and extended defects

Article history: Received - 26 January 2015 : Received in revised form 5 April 2015 : Accepted 28 April 2015 : Available online 4 June 2015Abstract not available.Sarabjeet Singh, Carl Q.Howard, Colin H.Hanse

Online condition monitoring of railway wheelsets

The rail industry has focused on the improvement of maintenance through the effective use of online condition monitoring of rolling stock and rail infrastructure in order to reduce the occurrence of unexpected catastrophic failures and disruption that arises from them to an absolute minimum. The basic components comprising a railway wheelset are the wheels, axle and axle bearings. Detection of wheelset faults in a timely manner increases efficiency as it helps minimise maintenance costs and increase availability. The main aim of this project has been the development of a novel integrated online acoustic emission (AE) and vibration testing technique for the detection of wheel and axle bearing defects as early as possible and well before they result in catastrophic failure and subsequently derailment. The approach employed within this research study has been based on the combined use of accelerometers and high-frequency acoustic emission sensors mounted on the rail or axle box using magnetic hold-downs. Within the framework of this project several experiments have been carried out under laboratory conditions, as well as in the field at the Long Marston Test Track and in Cropredy on the Chiltern Railway line to London