Particle filter-based prognostic approach for railway track geometry

Mishra, Madhav; Odelius, Johan; Thaduri, Adithya; Nissen, Arne; Rantatalo, Matti

doi:10.1016/j.ymssp.2017.04.010

Cited by 31 publications

(12 citation statements)

References 19 publications

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“…However, introduction of admissible stress values does not enable drawing any conclusions on the loading cycles and inter-repair terms. On the other hand, the methods based on phenomenological models [3,8,[9][10][11] do not take into consideration influence of the rolling stock and the track factors although they enable prediction of the track geometry impairment.…”

Section: розроблено метод розрахунку розладнання геометрIї колIї пIд mentioning

confidence: 99%

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Studying the railroad track geometry deterioration as a result of an uneven subsidence of the ballast layer

Nabochenko¹,

Sysyn²,

Kovalchuk³

et al. 2019

EEJET

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Section: розроблено метод розрахунку розладнання геометрIї колIї пIд mentioning

confidence: 99%

“…Statistical model of forecasting geometric state of switches based on the sequential Monte Carlo method was proposed in [9]. Unlike the conventional phenomenological models, this method enables obtaining of a probabilistic result of geometry impairment based on initial probabilistic values.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Studying the railroad track geometry deterioration as a result of an uneven subsidence of the ballast layer

Nabochenko¹,

Sysyn²,

Kovalchuk³

et al. 2019

EEJET

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“…Taking both estimation accuracy and convergence speed into account, the particle filter method is selected as the solver of equation (12). The basic idea of the particle filter algorithm is the Monte Carlo method, which approximates and replaces the probability density function of the system through a large number of random samples and selects a subset of particles for sequential importance sampling according to the posterior probability in order to shrink the scope of estimation [19][20][21][22]. Assume the target vector V LC :…”

Section: Parameter Recognition Methods Using the Gabormentioning

confidence: 99%

Parameter Recognition of Mode-Converted Wave in Single-Source Ultrasound Using Gabor Transform for Bolt Axial Stress Evaluation

2020

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In this paper, a novel parameter recognition method of mode-converted wave in single-source ultrasound is put forward and applied to the estimation of axial stresses in bolts. To overcome the distortion and aliasing of the mode-converted wave in single-source ultrasonic signal, a time-frequency parameter recognition method based on the Gabor transform is introduced to recognize the accurate time of flight (TOF) of the mode-converted wave. Based on the mode conversion of the single-source longitudinal ultrasound, a new nonlinear evaluation model based on acoustoelastic equation is derived to determine the axial stress of bolt. The performance of the proposed method is evaluated by comparing it with the commonly used TOF ratio method of longitudinal wave and shear wave (L-S). The experiment result shows that the proposed method is more effective in detecting the connection status of bolted joints than the traditional L-S method.

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“…Furthermore, PF and its enhanced versions are also extensively used for failure prognosis of different nonlinear systems [6], [14], [15]. In [6], PF is adopted to estimate the interturn fault parameters in the induction machine for RUL estimation.…”

mentioning

confidence: 99%

“…In [6], PF is adopted to estimate the interturn fault parameters in the induction machine for RUL estimation. In [14], an enhanced PF is developed for RUL prediction of tool wear. The number of particles is chosen to be time-varying which can reduce the computational cost.…”

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confidence: 99%

Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

Jiang

et al. 2019

IEEE Access

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This paper proposes a real-time model-based health monitoring method for a nonlinear mechatronic system with multiple faults in both parametric and nonparametric components. A nonlinear bond graph model incorporating the influence of Stribeck friction is established to capture the dynamic behavior of the monitored mechatronic system. Based on the model, fault diagnosis is carried out via the combinative fault signature matrix which is built from independent and dependent analytical redundancy relations to enhance the isolation ability of the monitored system under multiple-fault condition. After that, an adaptive genetic algorithm-particle filter (AGA-PF) is developed for fault parameter estimation and remaining useful life prediction. The AGA-PF can mitigate the sample impoverishment problem in generic particle filter through genetic operators with adaptive mutation probability according to the fitness of the particles. The effectiveness of the proposed method is verified through simulation and experiment investigations on the nonlinear mechatronic system. INDEX TERMS Fault diagnosis, genetic algorithm, particle filter, parameter estimation. I. INTRODUCTION

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Particle filter-based prognostic approach for railway track geometry

Cited by 31 publications

References 19 publications

Studying the railroad track geometry deterioration as a result of an uneven subsidence of the ballast layer

Studying the railroad track geometry deterioration as a result of an uneven subsidence of the ballast layer

Parameter Recognition of Mode-Converted Wave in Single-Source Ultrasound Using Gabor Transform for Bolt Axial Stress Evaluation

Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

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