Optimizing Railway Safety and Efficiency: A Comprehensive Review on Advancements in Outof-Round Wheel Detection Systems
Girmay Mengesha Azanaw
Girmay Mengesha Azanaw, Lecturer, Department of Civil Engineering, Institute of Technology, University of Gondar, Gondar, Ethiopia.
Manuscript received on 01 February 2025 | First Revised Manuscript received on 25 February 2025 | Second Revised Manuscript received on 06 March 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 38-52 | Volume-12 Issue-3, March 2025 | Retrieval Number: 100.1/ijies.A132405010525 | DOI: 10.35940/ijies.A1324.12030325
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Railway safety and operational efficiency represent fundamental cornerstones of contemporary transportation, demanding ongoing innovations in monitoring systems. This review thoroughly examines the latest advancements in out-ofround wheel detection technologies, which are essential in preventing derailments and reducing maintenance costs. The study synthesises progress in sensor technologies, including highresolution imaging, ultrasonic sensors, and acoustic emission detectors, which facilitate the early detection of wheel irregularities. By integrating these sensors with advanced signal processing algorithms and cutting-edge machine learning techniques, current systems can achieve real-time surveillance and predictive maintenance, thereby reducing the likelihood of catastrophic failures. The review evaluates diverse methodologies adopted for detecting out-of-round wheels, juxtaposing traditional manual inspection techniques with automated systems. It highlights the benefits of rapid data acquisition and the application of sophisticated analytics in enhancing detection accuracy across diverse environmental conditions. Furthermore, the discussion encompasses the challenges associated with sensor calibration, data noise, and the scalability of these systems within high-speed railway networks. Through a thorough assessment of experimental studies and real-world implementations, the review pinpoints key performance indicators and delineates the prospects of integrating these systems into existing railway safety protocols. It also emphasises the need for standardised benchmarks to evaluate system reliability and overall performance comprehensively. Looking towards the future, the paper suggests avenues for further research, such as the creation of multi-sensor fusion frameworks and adaptive algorithms to enhance diagnostic precision. Ultimately, these advancements have the potential to significantly enhance railway safety and operational efficiency, thereby contributing to the modernisation of global rail infrastructure.
Keywords: Out-of-Round (OOR) Wheel Detection, Railway Safety and Efficiency, Predictive Maintenance and Artificial Intelligence (AI) in Railways.
Scope of the Article: Civil Engineering and Applications