In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-...In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.展开更多
基金Projects(61490702,61773407,61803390,61751312)supported by the National Natural Science Foundation of ChinaProject(61725306)supported by the National Science Foundation for Distinguished Young Scholars of China+5 种基金Project(61621062)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of ChinaProject(2017TP1002)supported by Hunan Provincial Key Laboratory,ChinaProject(6141A0202210)supported by the Program of the Joint Pre-research Foundation of the Chinese Ministry of EducationProject(61400030501)supported by the General Program of the Equipment Pre-research Field Foundation of ChinaProject(2016TP1023)supported by the Science and Technology Project in Hunan Province Hunan Science and Technology Agency of ChinaProject(2018FJ34)supported by the Science and Technology Project in Shaoyang Science and Technology Agency of China
文摘In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.
