An integrated approach for the optimization of wheel–rail contact force measurement systems

Papini, Susanna; Pugi, Luca; Rindi, Andrea; Meli, Enrico

doi:10.1007/s40534-013-0013-z

Cited by 7 publications

(13 citation statements)

References 10 publications

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“…connecting the neurons of the hidden layer with the neurons of the output layer, and b (2) is the bias of the neuron of the output layer. The output y =f (x) is given by (2) (13) where a j is the output of the jth neuron of the hidden layer, and it can be expressed as…”

Section: A Model Of the Load Cell's Nonlinear Error Compensation Basmentioning

confidence: 99%

“…The output y =f (x) is given by (2) (13) where a j is the output of the jth neuron of the hidden layer, and it can be expressed as…”

Section: A Model Of the Load Cell's Nonlinear Error Compensation Basmentioning

confidence: 99%

“…We apply the augmented Lagrange multiplier method to solve (18) and construct the augmented performance index φ(ω, b (1) , v, b (2) , λ, σ )…”

Section: B Training Algorithm Of Almnnmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Lin

Wang

et al. 2015

IEEE Trans. Instrum. Meas.

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This paper presents a new approach for compensating nonlinear errors of a load cell, based on the optimal neural network with an augmented Lagrange multiplier (ALMNN). The load cell has serious nonlinear errors, which lower the accuracy of the weighing results. In this proposed approach, first, we construct the constraints for training the neural network (NN) using the load cell's prior knowledge, i.e., the monotonic increasing property of a load cell's input-output function. Second, we create the augmented Lagrange function with a multiplier and a penalty factor to serve as an NN's performance index, and then deduce the ALMNN's detailed training algorithm. The penalty factor plays an important role in ALMNN, thus the reasonable range of numerical value is discussed in this paper. Meanwhile, the weighing principle of a strain gauge load cell is introduced and its nonlinear error model is established. Finally, the experimental results demonstrate the effectiveness of ALMNN. By comparing with the conventional data induction method (DINN, i.e., the training of NN not with the prior knowledge but only the data samples), ALMNN significantly improves the NN's generalization ability, especially in the case of lacking training samples. In addition, the applicability of ALMNN is verified by three additional experiments.Index Terms-First-order derivative constraint, Lagrange multiplier, load cell, monotonicity, neural networks (NNs), nonlinear error compensation.

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Section: A Model Of the Load Cell's Nonlinear Error Compensation Basmentioning

confidence: 99%

“…The output y =f (x) is given by (2) (13) where a j is the output of the jth neuron of the hidden layer, and it can be expressed as…”

Section: A Model Of the Load Cell's Nonlinear Error Compensation Basmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Lin

Wang

et al. 2015

IEEE Trans. Instrum. Meas.

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“…When using dynamic tests to measure the dynamic indices of a track inspection vehicle and a new designed vehicle, we need instrumented wheelset and some accelerometers installed on the tested vehicles. However, the instrumented wheelset is known as an expensive device and involves a complicated calibration technology [1][2][3][4][5][6][7]. In addition, some structural modifications to the wheelset, e.g., drilling holes on the wheel hub, are necessary for the signal transmission and power supply, which means that this device is not suitable for long-term commercial operation of the train.…”

Section: Introductionmentioning

confidence: 99%

Safety evaluation for railway vehicles using an improved indirect measurement method of wheel–rail forces

Zeng

Lai

2016

J. Mod. Transport.

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The wheel-rail force measurement is of great importance to the condition monitoring and safety evaluation of railway vehicles. In this paper, an improved indirect method for wheel-rail force measurement is proposed to evaluate the running safety of railway vehicles. In this method, the equilibrium equations of a suspended wheelset are derived and the wheel-rail forces are then be obtained from measured suspension and inertia forces. This indirect method avoids structural modifications to the wheelset and is applicable to the long-term operation of railway vehicles. As the wheel-rail lateral forces at two sides of the wheelset are difficult to separate, a new derailment criterion by combined use of wheelset derailment coefficient and wheel unloading ratio is proposed. To illustrate its effectiveness, the indirect method is applied to safety evaluation of railway vehicles in different scenarios, such as the cross wind safety of a high-speed train and the safety of a metro vehicle with hunting motions. Then, the feasibility of using this method to identify wheel-rail forces for low-floor light rail vehicles with resilient wheels is discussed. The values identified by this method is compared with that by Simpack simulation for the same low-floor vehicle, which shows a good coincidence between them in the time domain of the wheelset lateral force and the wheel-rail vertical force. In addition, use of the method to determine the high-frequency wheel-rail interaction forces reveals that it is possible to identify the high-frequency wheel-rail forces through the accelerations on the axle box.

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“…때만 유효한 데이터가 측정된다 [2,3]. 비접촉센서를 이용하는 기법은 차륜 혹은 레일 롤러의 접촉점에서의 진동 변위를 측정하 여, 이 정보와 차륜의 진동특성을 이용하여 접촉력을 예측한다 [4].…”

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Study for Prediction of Contact Forces between Wheel and Rail Using Vibrational Transfer Function of the Scaled Squeal Noise Test Rig

Lee¹,

Kim²,

Ji³

et al. 2016

Journal of the Korean society for railway

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Curved squeal noise may result when railway vehicles run on curved tracks. Contact between the wheels and the rails causes a stick-slip phenomenon, which generates squeal noise. In order to identify the mechanism of the squeal noise systematically, a scaled test rig has been fabricated. Knowledge of the contact forces between the wheels and the rail rollers is essential for investigating the squeal noise characteristics; however, it is difficult to measure there contact force. In this study, contact forces have been calculated indirectly according to the modal behavior of the subframe that supports the rail roller and the responses at specific positions of that subframe. In order to verify the estimated contact forces, the displacements at the contact points between the wheels and rail rollers have been calculated from the estimated forces; the resulting values have been compared with the measured displacement values. The SPL at the specific location has been calculated using the estimated contact forces and this also has been compared with the SPL, measured in a semi-anechoic chamber. The comparisons in displacements and SPLs show good correlation.

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An integrated approach for the optimization of wheel–rail contact force measurement systems

Cited by 7 publications

References 10 publications

Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Safety evaluation for railway vehicles using an improved indirect measurement method of wheel–rail forces

Study for Prediction of Contact Forces between Wheel and Rail Using Vibrational Transfer Function of the Scaled Squeal Noise Test Rig

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