A time domain approach to load reconstruction for durability testing

Raath, Anton D.; Waveren, C.C.Van

doi:10.1016/s1350-6307(98)00008-9

Cited by 18 publications

(18 citation statements)

References 3 publications

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“…to lower the level of loading forces [14,24]. Load prediction in systems for which the force signal cannot be directly measured due to constructional constraints, as in the case of forces being exchanged by a wheel and the road or rail, is considered to be one of the most practical applications of the inverse approach [3,19].…”

Section: Theory Underlying Model Inversionmentioning

confidence: 99%

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

2011

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Requirements for current trains to be increasingly available have created the need to develop systems that can predict the quality of both trains and infrastructure components. The paper presents a new approach to the detection of rail truck irregularities, based on the measurements of bearing box acceleration during the operation of rail vehicles. The proposed procedure is based on an inverse problem solution, estimating track irregularities from measured acceleration of the applied model of vehicle dynamics. The simulation study of the proposed method, as well as its implementation, is presented. The method has been successfully applied for the identification of rail irregularities on a typical Polish railroad and vehicle.

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Section: Theory Underlying Model Inversionmentioning

confidence: 99%

“…The advantage of such a stabilization method is the lack of phase error and delay, while the only disadvantage is a small gain error that is, moreover, negligible if the output signal consists of low frequency components [22]. This stabilization technique was applied to load reconstruction by [3,19].…”

Section: Theory Underlying Model Inversionmentioning

confidence: 99%

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

2011

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“…to lower the level of loading forces [10][11][12]. Load prediction in systems for which the force signal cannot be directly measured due to constructional constraints [13], as in the case of forces being exchanged by a wheel and the road or rail, is considered to be one of the most practical applications of the inverse approach [14,15]. One of the other applications, proposed by [16], is reconstruction of the moving force of a bridge using the method of adaptive input estimation.…”

Section: Model Inversionmentioning

confidence: 99%

Application of an Inverse Data-Driven Model for Reconstructing Wheel Movement Signals

Czop¹

2011

Metrology and Measurement Systems

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This paper considers a method for indirect measuring the vertical displacement of wheels resulting from the road profile, using an inverse parametric data-driven model. Wheel movement is required in variable damping suspension systems, which use an onboard electronic control system that improves ride quality and vehicle handling in typical maneuvres. This paper presents a feasibility study of such an approach which was performed in laboratory conditions. Experimental validation tests were conducted on a setup consisting of a servo-hydraulic test rig equipped with displacement, force and acceleration transducers and a data-acquisition system. The fidelity and adequacy of various parametric SISO model structures were evaluated in the time domain based on correlation coefficient, FPE and AIC criteria. The experimental test results showed that inverse models provide accuracy of inversion, ranging from more than 70% for the ARX model structure to over 90% for the OE model structure.

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“…In the end of 1990s, Raath introduced parametric time domain models for simulating test systems with multiple axis [6]. He concluded that frequency domain methods require Fast Fourier Transform (FFT) analysis and identification data measurements with longer data sequences for attaining a model of high quality compared to time domain models, which achieve comparable accuracy with shorter measurements.…”

Section: Introductionmentioning

confidence: 99%

“…He concluded that frequency domain methods require Fast Fourier Transform (FFT) analysis and identification data measurements with longer data sequences for attaining a model of high quality compared to time domain models, which achieve comparable accuracy with shorter measurements. In multi-axial test systems, disturbances caused by cross-talking reduce the control accuracy of the individual channels [6,7]. An inverse system model capturing all of these mechanical coupling effects is essential for precise load data reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Optimization of inverse model identification for multi-axial test rig control

Muller

Ziegmann

Krüner

et al. 2016

MATEC Web of Conferences

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Abstract. Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO) model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.

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A time domain approach to load reconstruction for durability testing

Cited by 18 publications

References 3 publications

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

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

Application of an Inverse Data-Driven Model for Reconstructing Wheel Movement Signals

Optimization of inverse model identification for multi-axial test rig control

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