Application of inverse linear parametric models in the identification of rail track irregularities

Czop, Piotr; Mendrok, Krzysztof; Uhl, Tadeusz

doi:10.1007/s00419-010-0500-1

Cited by 34 publications

(12 citation statements)

References 16 publications

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“…It can be shown by applying the central limit theorem that the design of the obstacles in formula (57) is not irrelevant for a huge number N of summands in formula (58). The degree of freedom of this approach is systematically reduced by adapting the road-generating problem.…”

Section: Road Pre-processing For the Simulationmentioning

confidence: 99%

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Road and track irregularities: measurement, assessment and simulation

Haigermoser

Luber²,

Rauh

et al. 2015

Vehicle System Dynamics

100

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Road and track irregularities have an important influence on the dynamic behaviour of vehicles.Knowledge of their characteristics and magnitude is essential for the design of the vehicle but also for comparable homologation and acceptance tests as well as for the planning and management of track maintenance. Irregularities of tracks and roads are regularly measured using various measurement technologies. All have advantages and weaknesses and require several processing steps. Characterisation of irregularities is done in the distance as well as in the wavelength domain. For rail irregularities, various distance domain description methods have been proposed and are in use. Methods have been analysed and compared with regard to their processing steps. Several methods have been analysed using measured irregularity and vehicle response data. Characterisation in the wavelength domain is done in a similar way for track and road irregularities. Here, an important issue is the estimation of the power spectral densities and the approximation by analytical formulas. For rail irregularities, periodic defects also play an important role. The use of irregularities in simulations requires various processing steps if measured irregularities are used, as well as if synthetic data are utilised. This paper gives a quite complete overview of rail irregularities and points out similarities and differences to the road.

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Section: Road Pre-processing For the Simulationmentioning

confidence: 99%

“…[56] In [57], estimation is done in the frequency domain. In [58], system identification techniques are used to identify a parametric model or transfer functions between vertical track irregularity and accelerations in the vehicle. Several methods for the identification have been applied.…”

Section: Inertial Measurement Systemsmentioning

confidence: 99%

Road and track irregularities: measurement, assessment and simulation

Haigermoser

Luber²,

Rauh

et al. 2015

Vehicle System Dynamics

100

View full text Add to dashboard Cite

show abstract

“…Refs. [6,7] presented the use of the non-parametric/parametric system identification method to identify the wheel-rail force. Weston et al [3] applied a bogie-mounted pitch rate gyro to obtain the mean vertical track irregularities, and the axle box-mounted accelerometer was added when the wavelength of the irregularities was less than 6 m. Weston et al [8] then adopted a bogie-mounted yaw rate gyro to detect the mean lateral track irregularities.…”

Section: Introductionmentioning

confidence: 99%

“…The axle box acceleration measurement method [1][2][3][4][5][6][7][8] is widely applied to identify the track irregularity status. The advantages of this method [1,2] include the following: the measurement device is mounted on an in-service vehicle, the device is cheaper than an optical sensor and the rail irregularities are measured at the line speed.…”

Section: Introductionmentioning

confidence: 99%

Identification of the power spectral density of vertical track irregularities based on inverse pseudo-excitation method and symplectic mathematical method

Zhang¹,

Zhao

Zhang

et al. 2013

Inverse Problems in Science and Engineering

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A new method is proposed to identify the power spectral density (PSD) of vertical track irregularities. A vertical coupled vehicle-track dynamic model is established. In this model, a vehicle is simplified as a multi-rigid-body model with 10 degrees of freedom, and a track is treated as a three-layer discrete elastic support model in which the rail, which is supported discretely by the sleepers, is described by Bernoulli-Euler beam theory. The vehicle and track models are coupled by a linearized Hertzian spring. The axle box acceleration is selected as the measurement data, and the stationary PSD of the axle box acceleration is simply transformed into its displacement PSD. The PSD of the vertical track irregularities is identified using a combination of the inverse pseudo-excitation method and the symplectic mathematical method. The accuracy of the proposed method is investigated for the different measurement noise levels, vehicle speeds, primary suspension parameters and PSD classes. The numerical results indicate that the proposed method can accurately identify the PSD of the vertical track irregularities.

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“…Moreover, Jankowski considered the off-line recognition of spatial and temporal characteristics of a dynamic load [6]. Klinkov and Fritzen presented an updated overview of available time domain load reconstruction methods [7], while Czop et al proposed an approach for the detection of rail truck irregularities based on measurements of the bearing box acceleration [8]. Finally, Yu and Chan reviewed load recognition techniques for moving loads on bridges [9].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Recognition of Typical Loads in Low‐Speed Rotor System

Zhang

Zhao-jian

2017

Mathematical Problems in Engineering

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While the load variations within the low speed rotor systems affect the operating conditions and mechanical properties, they may also provide information on machine faults. Therefore, load recognition is of great significance in operational monitoring for detecting early warning signs of failure and diagnosing faults. In this paper, five types of typical loads in a low-speed rotor system are qualitatively analyzed. Moreover, a method is presented based on the vibration signals from a low-speed rotor system using the ensemble empirical mode decomposition (EEMD), energy feature extraction, and backpropagation neural network (BPNN). A low-speed rotor test bench was designed and manufactured for load recognition and an experiment was set up based on certain load characteristics. Loading tests for five representative categories were conducted and various vibration signals were collected simultaneously. The EEMD was shown to eliminate the mode mixing seen in traditional EMD, which resulted in a clear decomposition of the signal. Finally, the characteristics were imported into a BPNN after energy feature extraction, and the different types of load were accurately recognized. Comparing the experimental results to existing data, a total recognition rate of 92.38% was achieved, demonstrating that the proposed method is both reliable and efficient.

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Application of inverse linear parametric models in the identification of rail track irregularities

Cited by 34 publications

References 16 publications

Road and track irregularities: measurement, assessment and simulation

Road and track irregularities: measurement, assessment and simulation

Identification of the power spectral density of vertical track irregularities based on inverse pseudo-excitation method and symplectic mathematical method

Qualitative Recognition of Typical Loads in Low‐Speed Rotor System

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