A new friction condition identification approach for wheel–rail interface

Onat, Altan; Voltr, Petr; Lata, Michael

doi:10.1080/23248378.2016.1253511

Cited by 25 publications

(11 citation statements)

References 35 publications

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“…In the future work, authors plan to combine the swarm intelligence-based method with an unscented Kalman filter which is used for friction estimation [32]. Thus, the estimation results can be enhanced significantly by considering a state filter.…”

Section: Discussionmentioning

confidence: 99%

“…Spiryagin et al [27] highlighted that it is useful to consider adhesion estimation approaches for anti-slip control. The majority of these approaches are model-based [28] and they considered inverse dynamic modelling [29,30], family of Kalman filters [31][32][33][34], artificial neural network [35] and swarm intelligence-based method [36,37].…”

Section: Introductionmentioning

confidence: 99%

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A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

Zirek

Onat

2020

Rail. Eng. Science

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Anti-slip control systems are essential for railway vehicle systems with traction. In order to propose an effective anti-slip control system, adhesion information between wheel and rail can be useful. However, direct measurement or observation of adhesion condition for a railway vehicle in operation is quite demanding. Therefore, a proportional–integral controller, which operates simultaneously with a recently proposed swarm intelligence-based adhesion estimation algorithm, is proposed in this study. This approach provides determination of the adhesion optimum on the adhesion-slip curve so that a reference slip value for the controller can be determined according to the adhesion conditions between wheel and rail. To validate the methodology, a tram wheel test stand with an independently rotating wheel, which is a model of some low floor trams produced in Czechia, is considered. Results reveal that this new approach is more effective than a conventional controller without adhesion condition estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

Zirek

Onat

2020

Rail. Eng. Science

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“…Wheel/rail adhesion is a very complicated process that is a ected by many factors [27][28][29], including wheel/rail contact interface, locomotive velocity, and weather conditions. Although adhesion-creep characteristic curves vary under di erent working conditions [30], all adhesion-creep characteristic curves show similar change in trend characteristics; that is, each curve has an adhesion peak point, max .…”

Section: Wheel/rail Adhesion State Identification Model Strategymentioning

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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“…Some other recent versions of UKF in [49] – [51] are proposed to give more robust results when there are outliers, incorrect states and measurements. But, in our case since the RR state-space model can be formulated with two states and one parameter, the standard UKF and the modification of standard joint UKF (JUKF), which is widely used for parameter estimation [52] – [56] , are considered. In [57] , it is shown that the modification for JUKF, decreases the computational complexity which is called as modified joint unscented Kalman filter (MJUKF).…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the timevarying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method. INDEX TERMS Unscented Kalman filter, joint unscented Kalman filter, respiratory rate tracking, devicefree, vital signs, health monitoring, radio signals.

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A new friction condition identification approach for wheel–rail interface

Cited by 25 publications

References 35 publications

A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

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

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

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