An Ultrasonic Signal Denoising Method for EMU Wheel Trackside Fault Diagnosis System Based on Improved Threshold Function

Sun, Zhilin; Lu, Jingui

doi:10.1109/access.2021.3093482

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…In addition to stress-based methods, many sound-and image-based methods have also been used to detect wheel flats [85][86][87][88].…”

Section: Sound-or Image-based Wheel Flat Signal Acquisition Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

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Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel–rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

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“…In addition to stress-based methods, many sound-and image-based methods have also been used to detect wheel flats [85][86][87][88].…”

Section: Sound-or Image-based Wheel Flat Signal Acquisition Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

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“…Diagnosing wheel faults of electrical multiple units (EMU) presents a difficult challenge due to various noise interference caused by internal hazard defects of the rail. In this regard, Sun & Lu [24] developed an automated method of wheel fault detection, as shown in Figure 5, which is aided by a denoising algorithm to perform data quality enhancement. This technique consists of an efficient sine-type processing threshold function, which gives better results as compared to the classical wavelet hard/soft threshold function.…”

Section: A Ultrasonicmentioning

confidence: 99%

“…Ultrasonic and AE sensors are other types of sensors used in wayside inspection. However, the response speed is very slow and the vehicle needs to be slowed down to get more precise data [24] High-speed cameras aided by laser sensors on the other hand provide reliable, fast, and accurate information from the wayside about wheel profile and wheel tread defects. In addition to that, it also provides flexibility in terms of the portability of wayside systems [24], [38], [39], [72].…”

Section: B Design Challenges For Early Diagnosis 1) Sensing End Chall...mentioning

confidence: 99%

“…However, the response speed is very slow and the vehicle needs to be slowed down to get more precise data [24] High-speed cameras aided by laser sensors on the other hand provide reliable, fast, and accurate information from the wayside about wheel profile and wheel tread defects. In addition to that, it also provides flexibility in terms of the portability of wayside systems [24], [38], [39], [72]. These sensors with proper calibration and aided by powerful algorithmic techniques such as DL could be deployed alone for early detection of wheel tread defects and variations in wheel profile.…”

Section: B Design Challenges For Early Diagnosis 1) Sensing End Chall...mentioning

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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“…Additionally, the discrete wavelet transform is simple to calculate and easy to apply, and the proper selection of wavelet base types and decomposition depth can effectively complete the filtering. For wavelet transform denoising methods, there are currently maximum modulus methods [10], coefficient correlation methods [11], and threshold noise reduction methods [12], among which the most widely used method is the wavelet threshold denoising method, including hard and soft thresholding methods. However, the soft thresholding method involves numerous nonlinear calculations [13], which can significantly increase computation time with deeper wavelet decomposition layers, compromising real-time performance.…”

Section: Introductionmentioning

confidence: 99%

A Wavelet Transform-Based Real-Time Filtering Algorithm for Fusion Magnet Power Signals and Its Implementation

et al. 2023

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With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.

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An Ultrasonic Signal Denoising Method for EMU Wheel Trackside Fault Diagnosis System Based on Improved Threshold Function

Cited by 7 publications

References 37 publications

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

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

A Wavelet Transform-Based Real-Time Filtering Algorithm for Fusion Magnet Power Signals and Its Implementation

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