Real time estimation of the wheel-rail contact condtions using multi-Kalman filtering and fuzzy logic

Hussain, Imtiaz; Mei, T. X.; Mirzapour, Mohammad

doi:10.1109/control.2012.6334713

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Creep forces have a significant impact on the dynamics of the wheelset as they exhibit their presence when the wheel deviates from pure rolling [36]. The relevant equations of the motion of the wheelset for modeling include the longitudinal creep of the left and right wheels, the yaw movement, and the creep forces in the lateral direction and are given in Equations ( 1)- (4).…”

Section: Wheelset Dynamics and Modellingmentioning

confidence: 99%

“…The fuzzy logic identification system is used to analyze the data from the multiple model based on Kalman filters [4,52]. The normalized values of the residuals are used for the identification of the conicity level by producing numeric outputs associated with the models.…”

Section: Fuzzy Logic Identificationmentioning

confidence: 99%

“…Railway condition monitoring has witnessed several contributions from researchers in the form of signal-based and model-based techniques [1][2][3][4]. However, most of these techniques are applied to monitor the condition of the railway suspension system or wheel-rail adhesion, and researchers are still on the quest to explore ways to monitor wheel conicity.…”

Section: Introductionmentioning

confidence: 99%

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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: Wheelset Dynamics and Modellingmentioning

confidence: 99%

Section: Fuzzy Logic Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…1 shows the basic arrangement of wheelset of modern passenger carrying railway vehicle. The equations of motion of railway wheelset are expressed as [22]:…”

Section: Methologymentioning

confidence: 99%

Signal Based Indirect Wheel Profile Estimation Technique for Solid Axle Railway Wheelset

Shaikh

Kalwer

Mal

et al. 2021

Mehran Univ. res. j. eng. technol.

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Wheel profile is a very important factor that helps the steering performance of the railway vehicle. However, during the operation, the wheel profile tends to vary inconsistently due to frictional contact at wheel-rail interface. This paper focuses on the problem of railway wheelset profile and its alteration that affects the performance of railway vehicle. Signal-based indirect estimation technique using Fast Fourier Transform (FFT) is proposed to establish a relationship between dynamic response of vehicle and wheel profile. It is observed that changes in wheel profile has direct impact on the frequency of lateral dynamics. The effect of vehicle speed is also analyzed on the yaw and lateral motions of the wheelset. The effectiveness of the proposed technique in determining the relationship between the frequency of the oscillation and conicity level of the solid axle railway wheelset is demonstrated by developing simulation model in MATLAB and Simulink.

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“…In the data-based identi cation methods, the data-based model structure was pre-designed in advance, and the parameters of these model structures were optimized by using experimental data, and then, the trained models were used to identify wheel/rail adhesion states [12][13][14][15]. e family of the Kalman lter methods can minimise mean-squared estimation error for a linear stochastic system [17][18][19][20]. Arti cial neural networks (ANNs), KELM, and other network-based methods use predetermined numbers of neurons that are connected to each other to accomplish de ned actions [21,22].…”

Section: Introductionmentioning

confidence: 99%

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Liu

et al. 2020

Journal of Advanced Transportation

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The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

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Real time estimation of the wheel-rail contact condtions using multi-Kalman filtering and fuzzy logic

Cited by 8 publications

References 16 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

Signal Based Indirect Wheel Profile Estimation Technique for Solid Axle Railway Wheelset

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

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