Estimation of the statistical variation of crosstalk in wiring harnesses

Wang

2010 International Conference on Intelligent Computation Technology and Automation

et al. 2010

The crosstalk coupling for wiring harness in vehicle has great influence on electromagnetic compatibility (EMC), it is necessary to estimate it at the beginning of EMC design of system. The relative position between wires in harness is random, the deterministic methods based on constant harness geometry are inappropriate to estimate crosstalk, while the statistical methods can reflect the actual crosstalk coupling, reveal random nature of crosstalk. The paper described three kinds of statistical models of crosstalk, and each model has its own characteristic and scope of application: the worst case method is appropriate to find potential crosstalk quickly, both probabilistic model and segmentation method catch the quantitative statistical characteristics of crosstalk i.e. mean value and standard deviation, the segmentation method can estimate the crosstalk in non-uniform wiring harness particularly.

Section: Segmentation Methodsmentioning

confidence: 99%

Statistical Estimation of Crosstalk for Wiring Harness in Vehicle

Wang

2010 International Conference on Intelligent Computation Technology and Automation

et al. 2010

“…The extraction of the RLCG parameter matrix is an important step in solving the crosstalk problem of the non-uniform transmission line [14]. The statistical model of the RLCG parameter matrix is also a research hotspot for the field of crosstalk in harness layout [15], [16]. Shishuang Sun used the mathematical method of convolution and probability to process the RLCG parameter matrix of the hand-assembled cable bundle and made a range of predictions on the crosstalk [17].…”

Section: Introductionmentioning

confidence: 99%

Analysis on RLCG Parameter Matrix Extraction for Multi-Core Twisted Cable Based on Back Propagation Neural Network Algorithm

et al. 2019

A new method based on back propagation (BP) neural network for extracting RLCG parameter matrix of multi-core twisted cable is presented. With the properly selected parameter matrix sample, the variation characteristics of the parameter matrix of the multi-core twisted cable can be learned by the Levenberg-Marquardt (L-M) algorithm based on BP neural network. The proposed method is combined with the finite-difference time-domain (FDTD) method to calculate the near end crosstalk (NEXT) and the far end crosstalk (FEXT) of the multi-core twisted cable. To verify the new method, a three-core twisted cable is measured and analyzed in the frequency band of 100 kHz-1 GHz. The results show that the verification error of the extraction network of the RLCG parameter matrix has good accuracy, which does not exceed 0.8%. Compared with the full wave method, the maximum deviations of FEXT and NEXT solved by the proposed methods are −2.71 dB and 10.56 dB, respectively, which are better than 29.32 dB and 32.45 dB solved by the conventional method. INDEX TERMS Multi-core twisted cable, crosstalk, finite-difference time-domain (FDTD) method, neural network.

“…This method needs large repeated computations and the results are only suitable for the case studied. Many researches proposed the worst-case methods for transmission lines in some specific cases, such as weak coupling and strong coupling [8][9][10][11]. The worst-case methods can provide quick estimations for designs; however, the results may differ greatly from the actual values.…”

Section: Introductionmentioning

confidence: 99%

Influence Analysis of Stochastic Translation of Transmission Lines Over Ground

Xie¹,

Wang²,

Li³

et al. 2014

PIER Letters

Abstract-This paper proposes a method for the quick estimation of the average voltages at terminal loads when the transmission line translate randomly and analyzes the sensitivities of the loads' voltages to the translation. Because nonuniform transmission lines can be approximated as n-cascaded uniform lines, the study of uniform lines is the basis. Based on the transmission-line equations, the equations are derived to estimate the average voltages, the voltage variations, and the sensitivity of the voltage to the random translation when transmission lines have random translation in their cross sections. With these equations, the average voltages at the loads, the probability distributions of the voltage variations, and the sensitivity of the voltage to the random translation can be obtained quickly. A two-wire line over the ground is studied by using the proposed method. The average voltages and the voltage variations' probability distributions agree well with those via the Monte Carlo (MC) method and the proposed method is more efficient. The results show that the sensitivities of the voltages at the loads to the random height increase with the terminal sources but decrease with the height.