Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin

doi:10.1016/j.ymssp.2016.07.009

Cited by 112 publications

(70 citation statements)

References 26 publications

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“…where z and y are the longitudinal and vertical position of a certain point relative to the center of the wheel and is the nominal radius of a perfect wheel. According to the generation mechanism, a wheel with flats is approximately equivalent to cutting a plane from a certain position on the wheel tread [27]. Therefore, the equation of the flat (plotted in red) can be expressed as follows:…”

Section: Wheel-rail Force Simulation Modelmentioning

confidence: 99%

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

et al. 2020

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Featured Application: Wheel flats are one of the most threatening defects during the service lifespan of railway vehicle wheels. This study proposes a new long-term monitoring method for wheel flats based on multisensor arrays. The dynamic strain responses of rails are captured by sensor arrays mounted on the rail web, ensuring that all the wheels are assessed during the train passage. Through data fusion among multiple sensors, the method locates the specific position of wheel defects. This study provides potential guidance for the maintenance of vehicles soon after the occurrences of defects.Abstract: Wheel flats have become a major problem affecting the long-term service of railway systems. Wheels with flats create intermittent impact loads to trains and rails. This not only accelerates the deterioration of vehicle and track components but also leads to abnormal wheel-rail contact conditions. An effective method for detecting wheel conditions is urgently needed to ensure the operation of the railway and provide guidance for the repair of wheels. However, most previous researches have used qualitative detection methods, and hence have been unable to achieve accurate positioning of the wheel flats. In addition, the theoretical basis for the layout scheme for wheel flat detection sensors is lacking, making it impossible to meet the needs of field applications. In this study, we simulated the spatial distribution characteristics of rail strain, under different wheel flat conditions, and based on this, a layout scheme of multisensor arrays was proposed which more effectively captured the responses of the wheel flats. A wheel flat recognition and precise positioning method based on multisensor fusion was designed. The algorithm was validated through the combination of experimental and simulation methods. The result shows that the algorithm can ideally detect and locate the wheel flats under complex conditions.

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Section: Wheel-rail Force Simulation Modelmentioning

confidence: 99%

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

et al. 2020

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“…Next approach (Li, Zuo, Lin, & Liu, 2017) is using axle box vibration signal processing. In this study, an algorithm for adaptive multiscale morphological filtering (AMMF) was developed for on-board wheel flat monitoring.…”

Section: Wheel-flat Detection Systemsmentioning

confidence: 99%

Wheel-flat detection on trams using envelope analysis with Hilbert transform

Nowakowski

Komorski

Szymański

et al. 2019

Lat. Am. j. solids struct.

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The wheel-flat defect is one of the many issues decreasing the level of and comfort in light rail vehicles. The two main causes of wheel-flats are temporary or complete wheel blocking. The negative effect is the increase of the dynamic phenomenons in the wheel-rail interaction, which are impulsive in their nature. It could prove to be dangerous for the safety of the ride. Therefore, a regular wheel surface monitoring and wheel-flats detecting would be well-founded to the fast detection of a flat point and to take remedial measures. The main aim of the article is to present the algorithm, according to which the wheel-flat detection during tram passage is possible. Several vibration transducers were mounted on the rail and measured vibration amplitude during trams pass-by. The proposed method is based on the vibration signal processing in the time and frequency domain. The Hilbert transform was used in the algorithm and all research analyses. The carried out experimental study and the analysis of the results of the method, show a high efficiency in the wheel flat detection. KeywordsTram, wheel-flat, Hilbert transform, envelope analysis Wheel-flat detection on trams using envelope analysis with Hilbert transform Tomasz Nowakowski et al.

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“…The process of mathematical morphology in computational terms, consists of moving all the pixels of the image from left to right and from top to bottom in order to find isolated pixels, which are considered noise [12]. This noise is eliminated by applying erosion and dilation with the following equations:…”

Section: Application Of Morphological Operatorsmentioning

confidence: 99%

The Use of the Combination of Texture, Color and Intensity Transformation Features for Segmentation in the Outdoors with Emphasis on Video Processing

et al. 2019

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Segmentation is the first and most important part in the development of any machine vision system with specific goals. Segmentation is especially important when the machine vision system works under environmental conditions, which means under natural light with natural backgrounds. In this case, segmentation will face many challenges, including the presence of various natural and artificial objects in the background and the lack of uniformity of light intensity in different parts of the camera's field of view. However, today, we must use different machine vision systems for outdoor use. For this reason, in this study, a segmentation algorithm was proposed for use in environmental conditions without the need for light control and the creation of artificial background using video processing with emphasizing the recognition of apple fruits on trees. Therefore, a video with more than 12 minutes duration containing more than 22,000 frames was studied under natural light and background conditions. Generally, in the proposed segmentation algorithm, five segmentation steps were used. These steps include: 1. Using a suitable color model; 2. Using the appropriate texture feature; 3. Using the intensity transformation method; 4. Using morphological operators; and 5. Using different color thresholds. The results showed that the segmentation algorithm had the total correct detection percentage of 99.013%. The highest sensitivity and specificity of segmentation algorithm were 99.224 and 99.458%, respectively. Finally, the results showed that the processor speed was about 0.825 seconds for segmentation of a frame.

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Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

Cited by 112 publications

References 26 publications

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

Wheel-flat detection on trams using envelope analysis with Hilbert transform

The Use of the Combination of Texture, Color and Intensity Transformation Features for Segmentation in the Outdoors with Emphasis on Video Processing

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