Detection of Wheel Diameter Difference of Railway Wagon by ACMD-FBD and Optimized MKELM

Bo, Xie; Chen, Shiqian; Dong, Maoyong; Sui, Shunqi; Chang, Chao; Wang, Kaiyun

doi:10.1109/tim.2022.3201254

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Then, a machine learning algorithm, the Multiple Kernel Extreme Learning Machine (MKELM), was trained for feature classification between normal operation, anti- Another type of issue related to the wheels is their uneven wear in the same bogie, usually called wheel diameter difference (WDD), which is a recurring problem in wagons operating in heavy-haul lines. To monitor this condition, Xie et al [86] proposed the use of the lateral axle box acceleration signal and developed an automatic methodology, depicted in the framework of Figure 16, to extract in-phase WDD (on the same bogie side) and anti-phase WDD (on different sides of the bogie). The feature extraction was performed based on the combined use of the Adaptive Chirp Mode Decomposition (ACMD) signal decomposition algorithm and the calculation of the Fractal Box Dimension (FBD).…”

Section: Wheelset Damage Identificationmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.

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Section: Wheelset Damage Identificationmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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“…Then the portability and speed of the measuring equipment should be improved. The key dimensions of wheelsets are crucial to the safety of train operation, so they need to be detected in time [4][5][6][7][8][9]. The present main methods of measuring key dimensions of wheelsets have many categories.…”

Section: Introductionmentioning

confidence: 99%

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

Fang,

Wang,

Zhu

et al. 2024

Meas. Sci. Technol.

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The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. A rapid measurement algorithm for key dimensions of wheelsets based on image processing algorithms is studied. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.

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“…Traditional fault diagnosis methods mostly focus on the qualitative identification of faults, that is, identifying whether a certain fault exists. Xie et al [16] studied the detection about the WDD types (standard diameter, in-phase WDD, and antiphase WDD). However, the maintenance of the WDD depends on the value of the WDD rather than the type.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

Sui,

Wang,

Chen

2023

Meas. Sci. Technol.

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Tread wear is inevitable for railway vehicles. Because of the complicated railway condition, the wear rates of the two wheels of a wheelset are usually unequal, which leads to the wheel diameter difference (WDD). The WDD shortens the service life of the wheelset and deteriorates the dynamic performance of railway vehicles. Therefore, it has long been desired to monitor and quantify the WDD condition. However, influenced by the random irregularity, the effective vibration features induced by the WDD may be completely submerged in noise. In this paper, an adaptive TQWT decomposition method is proposed for denoise and feature enhancement. In this method, a new evaluation index is constructed and used to determine parameters. The influence of the WDD on the vibration of the wheelset can be more obvious after denoised by the adaptive TQWT decomposition. Mixed kernel principal component analysis is introduced into the condition monitoring and quantitative assessment of the WDD. By calculating the Hoteling’s T 2 and SPE statistics, control charts are employed to identify and assess the WDD conditions quantitatively in conjunction with the corresponding control limits. The effectiveness of the proposed method is validated by the simulation and experimental study. For the experimental datasets, the proposed method gives the average fault detection rate (FDR), false alarm rate (FAR), false alarm rate (PA) of 92.74%, 2.23%, 97.47% for the T 2 statistic and the average FDR, FAR, PA of 96.57%, 2.60%, 97.29% for the SPE statistic. The results show that the proposed method can effectively monitor the WDD conditions.

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Detection of Wheel Diameter Difference of Railway Wagon by ACMD-FBD and Optimized MKELM

Cited by 7 publications

References 38 publications

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

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