The effect of railway wheel wear on reprofiling and service life

Pires, A.C.; Pacheco, Lizbeth; Dalvi, I.L.; Endlich, C.S.; Queiroz, J.C.; Antoniolli, F.A.; Santos, Guilherme Fabiano Mendonça dos

doi:10.1016/j.wear.2021.203799

Cited by 13 publications

(11 citation statements)

References 25 publications

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“…Although great attention must be paid to the computational efficiency of the simulation, a direct quantitative comparison of the numerical models described in the previous section cannot be provided. In fact, the numerical tools shown above were Budapest University [65], KTH [38,66,67,[69][70][71][72][73], PoliMi [75], USFD [78], PoliMi + USFD [34,77,79], SWJTU [83], UNiFI + SWJTU [86], Others [88][89][90][91][92] Rail wear models: KTH [74], SWJTU [84] Wheel and rail wear models: Budapest University [36,64] Wheel wear models: AWARE [43,44,[94][95][96], UniFi [98][99][100][101][102]107], SWJTU [109,112], IIT [113][114][115]125], Others [93,97,111,116] Wheel and rail wear models: Un...…”

Section: Discussionmentioning

confidence: 99%

“…The UM wear module can update the wheel profiles to be used in the consecutive wear steps and it is also able to run several simulations in parallel and to apply weighing factors to account for different vehicle configurations, track layouts and speeds. The UM wear module was also exploited by Shadfar and Molatefi [90], to assess the influence of different parameters on the wheel wear evolution, as well as by Pires et al [91] in the context of the design of new wheel profiles. On the other hand, Smetanka et al [92] opted for the commercial software Simpack to run the wheel wear simulations; however the Simpack wear module is not able to automatically update the wheel profile by means of a simple graphic user interface once a wear step is over, so a code able to launch a cascade of Simpack simulations with updated profiles is required.…”

Section: Numerical Tools Not Relying On Co-simulation Techniquesmentioning

confidence: 99%

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Simulation of wheel and rail profile wear: a review of numerical models

2022

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The development of numerical models able to compute the wheel and rail profile wear is essential to improve the scheduling of maintenance operations required to restore the original profile shapes. This work surveys the main numerical models in the literature for the evaluation of the uniform wear of wheel and rail profiles. The standard structure of these tools includes a multibody simulation of the wheel–track coupled dynamics and a wear module implementing an experimental wear law. Therefore, the models are classified according to the strategy adopted for the worn profile update, ranging from models performing a single computation to models based on an online communication between the dynamic and wear modules. Nevertheless, the most common strategy nowadays relies on an iteration of dynamic simulations in which the profiles are left unchanged, with co-simulation techniques often adopted to increase the computational performances. Work is still needed to improve the accuracy of the current models. New experimental campaigns should be carried out to obtain refined wear coefficients and models, while strategies for the evaluation of both longitudinal and transversal wear, also considering the effects of tread braking, should be implemented to obtain accurate damage models.

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Section: Discussionmentioning

confidence: 99%

Section: Numerical Tools Not Relying On Co-simulation Techniquesmentioning

confidence: 99%

Simulation of wheel and rail profile wear: a review of numerical models

2022

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“…The measurement position of WDD is at the nominal rolling circle. To eliminate the WDD, the wheelset with WDD should be reprofiled by cutting the tread, which shortens the service life of the wheelset [20]. It is crucial to identify the WDD condition of the wheelset accurately and timely.…”

Section: Problem Statementmentioning

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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“…Currently, the maintenance planning of hollow worn wheels relies mostly on frequent visual inspections, where measurements of the wheel profile are collected by skilled technicians using special tools with the vehicle being out of normal operation. If the wheel profile shape parameters are within specific standard limits, the vehicle continues its normal operation; otherwise, its wheels are reprofiled [23]. A standard measurement is the depth of the hollow to the center of the wheel tread, which is not allowed to be over ∼3 mm [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…If the wheel profile shape parameters are within specific standard limits, the vehicle continues its normal operation; otherwise, its wheels are reprofiled [23]. A standard measurement is the depth of the hollow to the center of the wheel tread, which is not allowed to be over ∼3 mm [22,23].…”

Section: Introductionmentioning

confidence: 99%

On-Board Random Vibration-Based Robust Detection of Railway Hollow Worn Wheels under Varying Traveling Speeds

Kaliorakis,

Sakellariou,

Fassois

2023

Machines

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The problem of the prompt detection of early-stage hollow worn wheels in railway vehicles via on-board random vibration measurements under normal operation and varying speeds is investigated. This is achieved based on two unsupervised statistical time series (STS) methods which are founded on a multiple-model (MM) framework for the representation of healthy vehicle dynamics. The unsupervised MM power spectral density (U-MM-PSD) method employs Welch-based PSD estimates for wheel wear detection and the unsupervised MM autoregressive (U-MM-AR) method for the parameter vectors of multiple AR models. Both methods are assessed via two case studies using thousands of test cases. The first case study includes Monte Carlo simulations using a SIMPACK-based detailed railway vehicle model, while the second is based on field tests with an Athens Metro train. Wheel wear detection is pursued using lateral or vertical vibration signals from the bogie or the carbody of a trailed vehicle traveling with three different speeds (60, 70, 80 km/h) using wheels under healthy conditions or with early stage hollow wear. Both methods exhibit remarkable performance with the U-MM-AR method to achieve the best overall results, reaching correct detection rates of even 100% with false alarm rates below 5% based on a single accelerometer either on the carbody or bogie.

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The effect of railway wheel wear on reprofiling and service life

Cited by 13 publications

References 25 publications

Simulation of wheel and rail profile wear: a review of numerical models

Simulation of wheel and rail profile wear: a review of numerical models

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

On-Board Random Vibration-Based Robust Detection of Railway Hollow Worn Wheels under Varying Traveling Speeds

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