Rail Weld Defect Prediction and Related Condition-Based Maintenance

Yao, Nan; Jia, Yong; Tao, Kai

doi:10.1109/access.2020.2999385

Cited by 7 publications

(2 citation statements)

References 37 publications

(48 reference statements)

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“…An Extreme Learning Machine is a single-hidden-layer feedforward neural network algorithm, that is effective and easy to use. In comparison to traditional neural networks, ELMs generally provide faster results and have increased learning speeds and more generalization [25].…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

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Predictive Maintenance for Railway Domain: A Systematic Literature Review

Mario

Mezhuyev

Tschandl

2023

IEEE Eng. Manag. Rev.

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Railways are considered to be an environmentally friendly and efficient means of transport for people and goods with increasing importance in the transport policies of many countries. However, the infrastructure and the substantial demand for maintenance create additional costs for railway operators. To overcome outdated maintenance modes, implementation of new solutions, optimization of maintenance activities and resource utilization are required. Through a systematic literature review (SLR), this work evaluates new approaches toward implementing predictive maintenance in the railway domain. A comprehensive search, including the IEEE Xplore, Science Direct, and ACM Digital Library has been conducted, focusing on papers related to predictive maintenance and railway systems, published in peerreviewed journals since 2016. The selected papers were analysed and grouped to allocate the research purposes as well as the considered assets, components, predicted defects, and maintenance conditions. Furthermore, the utilized predictive maintenance algorithms and their limitations are structured and evaluated. Analysis shows that a great variety of algorithms were used for either defect detection or the prediction of conditions of 20 different components, which are critical for the safety and availability of railway operations. The study shows that the proposed approaches were successfully tested and yielded great potential for predictive maintenance solutions. Researchers state to enhance proposed solutions within their future work, increasing accuracy, performance, and widening the area of application in the railway domain. MANAGERIAL RELEVANCE STATEMENTThe paper discusses new predictive maintenance solutions, which have significant potential for saving costs through optimization of resource consumption, reducing the labour intensiveness of maintenance activities, and decreasing effortful tasks such as inspections and patrolling. Implementing the suggested solutions will improve the availability of the railway network, reduce costs related to train interruptions, and simultaneously improve customer satisfaction. Increasing the safety of the railway network through early detection of defects reduces the risk of component-related accidents, as well as the risk of human error, for example leaving defects undetected during long and tiring inspections or patrolling tasks. In addition, workplace health and safety will improve as being present on-site becomes less of a necessity, hence lowering the risk of accidents occurring during maintenance works.

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Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

“…An ELM was utilized to predict rail weld defects with a 100% recall rate, based on a test dataset, allowing for decreased workloads, related to inspections, while maintaining the safety requirements [25].…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%