An Efficient Method for Wheel-Flattened Defects Detection Based on Acoustic Emission Technique

Chang, Yongqi; Zhang, Xin; Chun-hu, Lin; Shen, Yi

doi:10.1109/tuffc.2021.3138197

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Furthermore, the interference of the wheel-rail rolling mechanism crucially affects the detection accuracy of wheel faults. To solve the aforementioned problem, Chang et al [48] proposed a novel method of detection, as illustrated in Figure 17, which includes an Improved Synthesize Health Index (ISHI) with a Time-Adaptive Threshold (time-ATH). To acquire the information regarding faults from wheel-generated signals, a complete set of features containing multiple types of features was extracted from AE signals.…”

Section: E Acoustic Emissionmentioning

confidence: 99%

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State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

et al. 2023

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In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

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Section: E Acoustic Emissionmentioning

confidence: 99%

“…FIGURE 18.Experiment condition (a) system structure of AE experiment, (b) experiment equipment, (c) receiving sensor and mechanical clamp, and (d) pre-amplifier[48] …”

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confidence: 99%