Research on Numerical Simulation Method of Nonstationary Random Vibration Signal Sensor in Railway Transportation

During railway transportation, due to various factors such as road conditions and operating conditions and produced vibrations and shocks, this kind of vibration environment may cause fatigue damage to on-board equipment and transported goods. The authors propose a research on the numerical simulation method of the nonstationary random vibration signal sensor of railway transportation; first, they establish the mathematical model of the railway nonstationary random vibration signal sensor and then introduce the method of reconstructing the railway nonstationary random vibration signal sensor. For railway nonstationary non-Gaussian random vibration reconstruction signal, compare the time-domain characteristics of the sampled signal, and for railway nonstationary non-Gaussian random vibration reconstruction signal, compare the frequency domain characteristics of the sampled signal. The results show that the relative error of the RMSM function is within 6%, the relative error of the sliding bias function is within 10%, and the relative error of the sliding kurtosis function is within 8%. The energy distribution of the edge Hilbert amplitude spectrum is very similar, with absolute error less than 6%. The energy fluctuations are similar in each band, with absolute error rates less than 4% in most bands. The method proposed in this article, suitable for reconstruction of railway nonstationary Gaussian random vibration and nonstationary non-Gaussian vibration signal sensor, verifies the effectiveness and feasibility of the signal reconstruction method. The model and signal reconstruction method proposed in this paper are applied to the railway nonstationary Gaussian and nonstationary non-Gaussian random vibration sampling signals.

[1]  Li Li,et al.  Controlling messy errors in virtual reconstruction of random sports image capture points for complex systems , 2021 .

[2]  Sachi Nandan Mohanty,et al.  Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management , 2021, Electronics.

[3]  Gaurav Dhiman,et al.  Line Monitoring and Identification Based on Roadmap Towards Edge Computing , 2021, Wirel. Pers. Commun..

[4]  Shu-Zheng Yang,et al.  Research on the semiclassical theory and Hawking tunneling radiation of nonstationary Kerr black hole , 2020 .

[5]  M. Ghadiri,et al.  Size-dependent random vibration analysis of AFM probe with tip mass considering surface viscoelastic effect , 2019, The European Physical Journal Plus.

[6]  Pawandeep Kaur,et al.  Study of Issues and Challenges of Different Routing Protocols in Wireless Sensor Network , 2019, 2019 Fifth International Conference on Image Information Processing (ICIIP).

[7]  K. Zhou,et al.  The random vibration and force transmission characteristics of the elastic propeller-shafting system induced by inflow turbulence , 2019, Ocean Engineering.

[8]  Antonina Pirrotta,et al.  Random vibration mitigation of beams via tuned mass dampers with spring inertia effects , 2019, Meccanica.

[9]  Lin Yang,et al.  Comparison of fatigue life prediction methods for solder joints under random vibration loading , 2019, Microelectronics Reliability.

[10]  David Kennedy,et al.  An uncertain computational model for random vibration analysis of subsea pipelines subjected to spatially varying ground motions , 2019, Engineering Structures.

[11]  Tao Tang,et al.  Failure study of Sn37Pb PBGA solder joints using temperature cycling, random vibration and combined temperature cycling and random vibration tests , 2018, Microelectron. Reliab..

[12]  I. Elishakoff,et al.  Critical comparison of exact solutions in random vibration of beams using three versions of Bresse–Timoshenko theory , 2018, Probabilistic Engineering Mechanics.

[13]  Vinod Sharma,et al.  On Sequential Random Distortion Testing of Non-Stationary Processes , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Suresh K. Sitaraman,et al.  Random vibration analysis of 3-Arc-Fan compliant interconnects , 2018, Microelectron. Reliab..

[15]  Xiaoling Jin,et al.  Approximately analytical technique for random response of LuGre friction system , 2017, International Journal of Non-Linear Mechanics.

[16]  Abdelkhalak El Hami,et al.  Probabilistic fatigue damage estimation of embedded electronic solder joints under random vibration , 2017, Microelectron. Reliab..

[17]  A. Dogariu,et al.  Wide-field interferometric measurements of nonstationary complex coherence function , 2017, 2017 IEEE Photonics Conference (IPC).

[18]  S. R. Lopes,et al.  Detection of nonstationary transition to synchronized states of a neural network using recurrence analyses. , 2017, Physical review. E.

[19]  B. Y. Ni,et al.  A Monte Carlo simulation method for non-random vibration analysis , 2017 .

[20]  Fei Wang,et al.  Electrostatic energy harvesting device with dual resonant structure for wideband random vibration sources at low frequency. , 2016, The Review of scientific instruments.

[21]  Yu Jiang,et al.  Fatigue life prediction model for accelerated testing of electronic components under non-Gaussian random vibration excitations , 2016, Microelectron. Reliab..

[22]  Huajiang Ouyang,et al.  Random vibration of an elastic half-space subjected to a moving stochastic load , 2016 .

[23]  Chang-Sheng Lin,et al.  Ambient modal identification using non-stationary correlation technique , 2016 .

[24]  Zhen Wang,et al.  Numerical simulation and fatigue life estimation of BGA packages under random vibration loading , 2015, Microelectron. Reliab..

[25]  John H. L. Pang,et al.  Study on reliability of PQFP assembly with lead free solder joints under random vibration test , 2015, Microelectron. Reliab..

[26]  Yonghong Wu,et al.  Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis , 2015 .

[27]  Habib Hajimolahoseini,et al.  Instantaneous fundamental frequency estimation of non-stationary periodic signals using non-linear recursive filters , 2015, IET Signal Process..

[28]  Alberto Di Matteo,et al.  Optimal tuning of tuned liquid column damper systems in random vibration by means of an approximate formulation , 2015 .

[29]  Şaban Çetin,et al.  Nonlinear adaptive control of semi-active MR damper suspension with uncertainties in model parameters , 2015 .

[30]  Gang Wang,et al.  A self-consistent model for the elastic contact of rough surfaces , 2015 .

[31]  V. J. Majd,et al.  An ISM-based CNF tracking controller design for uncertain MIMO linear systems with multiple time-delays and external disturbances , 2015 .

[32]  Sameer B. Mulani,et al.  Generalized Linear Random Vibration Analysis Using Autocovariance Orthogonal Decomposition , 2010 .

[33]  S. Subba Rao On some nonstationary, nonlinear random processes and their stationary approximations , 2006, Advances in Applied Probability.

[34]  Wei Gao,et al.  Reliability-Based Optimization of Active Nonstationary Random Vibration Control , 2005 .

[35]  B. H. K. Lee,et al.  Neural Network Parameter Extraction with Application to Flutter Signals , 1998 .