Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

Mosleh, Araliya; Montenegro, Pedro Aires; Costa, Pedro Alves; Calçada, Rui

doi:10.3390/app11094002

Cited by 39 publications

(33 citation statements)

References 49 publications

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“…Wear of the wheelset is an inevitable phenomenon due to the failure in maintaining acceptable conicity and avoiding collision between the wheel flange and rail [26][27][28][29]. Another recent study discusses early detection of wheel damage resulting in lower maintenance costs [30]. In this work, a wayside monitoring strategy is adopted for wheel flat detection using sensors with the emphasis on their optimal position of installation.…”

Section: Conicity Managementmentioning

confidence: 99%

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

et al. 2021

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The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy.

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Section: Conicity Managementmentioning

confidence: 99%

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

et al. 2021

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“…To set up the equivalent rail/track defect, which corresponds to the wheel flat, the Haversine function may be used. This function is expressed in Equation (5). In this equation, z and x correspond respectively to the vertical and the longitudinal direction of the track.…”

Section: Wheel Flat Modelingmentioning

confidence: 99%

“…In order to analyze the influence of the depth of the wheel flat on the magnitude of the dynamic force on the track, wheel flats with depths from 0 to 2 mm at intervals of 0.5 mm were analyzed. The flat length l depends on the flat depth d as shown in Equation (5). For this calculations, a train speed of 360 km/h was considered, and the results are presented in Table 3.…”

Section: Ballasted Trackmentioning

confidence: 99%

“…Mosleh et al [4] concluded that the envelope spectrum analysis is a capable tool and a cost-effective method that can be used to detect wheel flats along with the flat impact frequency for different train speeds in real-world conditions. Mosleh et al [5] ran a 3D numerical simulation of the train track dynamic response to the presence of wheel flats to identify the type of sensors that can be adopted in wayside wheel flat detection, but this study focused only on ballasted tracks. The EU-funded MAXBE Project (http://www.maxbeproject.eu/, accessed on July 2021), which focused on detecting wheel flats and axle bearing failure modes at an early stage [6], also presented relevant methodologies for wayside and on-board vehicle monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks

Vale

2021

Applied Sciences

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Wheel flats induce high-impact loads with relevance for the safety of the vehicle in operation as they can contribute to broken axles, hot axle boxes, and damaged rolling bearings and wheels. The high loads also induce damage in the track components such as rails and sleepers. Although this subject has been studied numerically and experimentally over the last few years, the wheel flat problem has focused on ballasted tracks, and there is a need to understand the phenomena also for slab tracks. In this research, a numerical approach was used to show the effects of the wheel flats with different geometric configurations on the dynamic behavior of a classical ballasted track and a continuous slab track. Several wheel flat geometries and different vehicle speeds were considered. The nonlinear Hertzian contact model was used because of the high dynamic variation of the interaction of the load between the vehicle and the rail. The results evidenced that, for the same traffic conditions, the dynamic force was higher on the slab track than on the ballasted one, contrary to the maximum vertical displacement, which was higher on the ballasted track due to the track differences regarding the stiffness and frequency response. The results are useful for railway managers who wish to monitor track deterioration under the regulatory limits.

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“…One method is based on novel wavelet features to classify time series data by support vector machine (SVM); the other method trains the convolutional neural networks (CNN) for different defect types to predict if a wheel has a defect during regular operation. Mosleh et al [9] established a 3D numerical dynamic model of a vehicle-track coupling system and analyzed the sensitivity and reliability of different sensors and setups for wheel flat detection. In their method, the wheel flat was identified by the envelope spectrum approach with spectral kurtosis analysis by considering the evaluated shear and accelerations in 19 positions as inputs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

et al. 2021

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Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

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Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

Cited by 39 publications

References 49 publications

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

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