Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems

Tam, Hwa Yaw; Lee, Kang Kuen; Liu, Shun-Yee; Cho, Lok-Hin; Cheng, K.W.E.

doi:10.1109/icirt.2018.8641602

Cited by 9 publications

(4 citation statements)

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“…An optical fiber sensing network was also depicted in Tam et al [ 305 ]; the authors developed a condition-monitoring system based on machine learning in order to detect and identify various types of track defects (rail corrugations, dipped weld joints, and defects of rail crossings).…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…ANN layers are shown in Figure 1 [34]. [11] On board Equipment ANN, DL Analyzing the fault diagnosis of train  X De bruin et al [12] Railway Line RPNN Classify and Diagnose the fault  X Shebani ve Iwnicki [13] Wheels and Rails ANN Estimating wheel and rail wear  X Kaewunruen [14] Wheels and Rails M, S Estimating wheel and rail wear   Zhao et al [15] Bogie faults DNN Diagnose faults of the bogie  X Gibert et al [16] Connectors DL Detecting defects on rail fasteners  X Tam et al [17] Rails ML Estimating wheel and rail wear  X Lidén and Joborn [18] Trains MILP Maintenance planning optimization X  Luan et al [19] Infracture MILP, LR Min the deviation of arrival times from an ideal timetable X  Su et al [20] Infracture TIO,CCO,NCO Infrastructure Maintenance planning optimization   Baldi et al [21] Trains GA Maintenance planning optimization X  Zhang et al [22] Trains MILP Min the total travel time of trains   Consilvio et al [23] Trains SS Maintenance planning optimization   Peralta et al [24] Railway Line GA, MOA Maintenance planning optimization   Xu et al [25] Trains QT Optimizing advanced maintenance cycle   Verbert et al [26] Railway Network POM profit by spreading or combining various maintenance activities   Kaewunruen and Chiengson [27] Wheels and Rails S Examining the effects of wheels and rail on railway line inspection and maintenance priorities   An ANN model includes an input layer, hidden layer, and output layer, which can be modeled according to the problem type. It is the input layer where the input data is located and the data transmission to the ANN is provided.…”

Section: Methodsmentioning

confidence: 99%

“…Gibert et al [16] proposed the automatic part inspection using the computer's vision and pattern recognition methods to detect faults of fasteners with the help of multiple detectors. Tam et al [17] demonstrated the optical fiber detection network-based railway condition monitoring system which can facilitate predictive maintenance on railways. Learning models that can be used to detect and identify different types of part defects such as machine learning, track grooves, weld joints, and rail transitions were developed.…”

Section: Literature Reviewmentioning

confidence: 99%

An Artificial Neural Network Model for Maintenance Planning of Metro Trains

et al. 2021

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In urban transportation, trains have an increasingly important place due to the increase in the number of passengers. Meeting the number of passengers is directly related to the number of trains operated on a line. Thus, the frequency of operation of trains affects the level of wear of the equipment. This makes train maintenance more important. Equipment faults are the basis for train maintenance. However, the fault times of the equipment which are unknown causes uncertainty in the maintenance activities and plans. This uncertainty results from many factors that affect the faults of the train. If historical maintenance data, fault data, and factors affecting the faults are known, effective use of resources (time, cost and personnel, etc.) is provided and uncertainty is eliminated. In this study, firstly, maintenance data in Ankara Metro between 2017 and 2018 is examined and the factors affecting equipment faults are evaluated with expert opinion. Artificial Neural Network (ANN) model is created with the data set and this data set along with the factors affecting each the equipment fault according to the type of equipment. In the ANN model, 5 factors (Equipment Type, Preventive Maintenance Frequency, Material Quality, Life Cycle, Line Status) affecting the faults of the equipment is determined as inputs and the number of failures as outputs. The mean absolute percent error (MAPE) value is found as 11%, and the mean square error value (MSE) is 0.0028229 in the training and test stages of ANN. Then, the frequency of fault is found according to the equipment fault and a 10-week maintenance planning is applied. The results are compared with current maintenance planning. As a result of the applied maintenance planning, the average number of faults of the trains decreases by 27%, uninterrupted service rate increases by 40% and heavy maintenance errors are also prevented. Fault removal times resulted in a 10% improvement. The results showed that ANN models could be used effectively in fault prediction and maintenance planning with rail system multiple types of equipment. In the literature, there is no study that implements maintenance planning with an ANN model where all train equipments and factors affecting the failure are evaluated together. This study is the first in the field of rail systems maintenance in the literature and will be a reference for future studies.

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“…For example, the versatility of optical fiber Bragg grating sensors is utilized in monitoring high voltage overhead lines, rail corrugations, and wheel-rail interactions. The advantages of fiber sensors are immunity to electromagnetic interference, multiplexing capabilities, long reach, lightweight, and high signal fidelity [30]. Wayside detectors can also be used in environmental and meteorological monitoring, such as temperature sensors and weather recorders.…”

Section: Data Acquisition In Railway Track Engineeringmentioning

confidence: 99%

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

et al. 2020

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Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

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Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems

Cited by 9 publications

References 0 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

An Artificial Neural Network Model for Maintenance Planning of Metro Trains

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

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