Adaptive and Sparsest Time-frequency Analysis Method Based on Genetic Algorithm and Its Applications

Abstract

Abstract:

To solve the choice of initial phase function, ASTFA method was proposed herein based on GA. The initial phase function of ASTFA was optimized by GA. The ability of avoiding mode mixing was studied. The analysis shows that the GA-ASTFA method has the ability of avoiding mode mixing and can suppress the pseudo components. And the component's accuracy decomposed via GA-ASTFA is better. Finally, the GA-ASTFA was applied to rub-impact fault diagnosis in rotor systems. Experimental analyses show that the GA-ASTFA method can extract the fault feature information effectively.

Key words: genetic algorithm(GA), adaptive and sparsest time-frequency analysis(ASTFA), empirical mode decomposition(EMD), mode mixing, rub-impact fault

摘要：

为解决自适应最稀疏时频分析（ASTFA）方法中初始相位函数的选择问题，采用遗传算法（GA）对ASTFA的初始相位函数进行优化，提出了GA-ASTFA方法。进一步研究了GA-ASTFA方法抑制模态混淆的能力，分析结果表明，GA-ASTFA能较好地抑制模态混淆，分解得到的分量信号精度高，且可抑制分解中的伪分量。最后将GA-ASTFA方法用于转子碰摩故障诊断，实验分析结果表明，GA-ASTFA方法能有效提取转子碰摩故障特征信息。

关键词: 遗传算法, 自适应最稀疏时频分析, 经验模态分解, 模态混淆, 转子碰摩

CLC Number:

Li Baoqing, Cheng Junsheng, Wu Zhantao, Peng Yanfeng. Adaptive and Sparsest Time-frequency Analysis Method Based on Genetic Algorithm and Its Applications[J]. China Mechanical Engineering, 2016, 27(01): 66-72.

李宝庆, 程军圣, 吴占涛, 彭延峰. 基于GA的自适应最稀疏时频分析方法及应用[J]. 中国机械工程, 2016, 27(01): 66-72.

References

[1]胡茑庆.转子碰摩非线性行为与故障辨识的研究[D].长沙：国防科技大学，2001.
[2]程军圣，于德介，杨宇. EMD方法在转子局部碰摩故障诊断中的应用[J]. 振动、测试与诊断，2006，26(1)：24-27.
Cheng Junsheng，Yu Dejie，Yang Yu. Application of EMD to Local Rub-impact Fault Diagnosis in Rotor Systems[J]. Journal of Vibration, Measurement & Diagnosis，2006，26(1)：24-27.
[3]Huang N E，Shen Z，Long S R.The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J].Proceedings of the Royal Society A，1998，454：903-995.
[4]高云超，桑恩方，许继友. 分离EMD中混叠模态的新方法[J]. 哈尔滨工程大学学报，2008，29(9)：963-966.
Gao Yunchao，Sang Enfang，Xu Jiyou. A New Method for Separating Mixed Modes in Empirical Mode Decomposition[J]. Journal of Harbin Engineering University，2008，29(9)：963-966.
[5]Wu Z，Huang N E. Ensemble Empirical Mode Decomposition：a Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis，2009，1(1)：1-41.
[6]Chu P C，Fan C，Huang N E. Compact Empirical Mode Decomposition：An Algorithm to Reduce Mode Mixing，End Effect，and Detrend Uncertainty[J]. Advances in Adaptive Data Analysis，2012，4(3)：1-18.
[7]郑近德，程军圣，曾鸣，等. 基于伪极值点假设的经验模态分解及其在转子故障诊断中的应用[J]. 中国机械工程，2014，25(18)：2467-2472.
Zheng Jinde，Cheng Junsheng，Zeng Ming，et al. Pseudo-extrema Based EMD and Its Application to Rotor Fault Diagnosis[J]. China Mechanical Engineering，2014，25(18)：2467-2472.
[8]杨宇，曾鸣，程军圣. 局部特征尺度分解方法及其分解能力研究[J]. 振动工程学报，2012，25(5)：602-609.
Yang Yu，Zeng Ming，Cheng Junsheng. Research on Local Characteristic-scale Decomposition and Its Capacities[J]. Journal of Vibration Engineering，2012，25(5)：602-609.
[9]Rilling G，Flandrin P. One or Two Frequencies? The Empirical Mode Decomposition Answers[J]. IEEE Trans. Signal Processing，2008，56(1)：85-95.
[10]Hou T Y，Shi Z Q. Adaptive Data Analysis via Sparse Time-frequency Representation[J]. Advances in Adaptive Data Analysis，2011，3(1/2)：1-28.
[11]Hou T Y，Shi Z Q. Data-driven Time-frequency Analysis[J]. Applied and Computational Harmonic Analysis，2013，35(2)：284-308.
[12]Hou T Y，Shi Z Q，Peyman T. Convergence of a Data-driven Time-frequency Analysis Method[J]. Applied and Computational Harmonic Analysis，2014，37(2)：235-270.
[13]David M，Alessandro C，James T，et al. Compressed Sensing Techniques for Detecting Damage in Structures[J]. Structural Health Monitoring，2013，12(4)：325-338.

[1]	HU Yu1, HE Zhicheng1, ZHAO Liang2. Time-domain TPA Method Based on AKFZ [J]. China Mechanical Engineering, 2024, 35(09): 1698-1709.
[2]	LIU Xuemei；LIU Tao；GUO Huan. Optimization of Buffer Allocations in Hybrid Production Lines Based on Availability Evaluation [J]. China Mechanical Engineering, 2020, 31(18): 2220-2230.
[3]	GE Liang, MIAO Rui, GE Qiuyuan, WU Yizhou. Feature Extraction and Defect Identification of Eddy Current Testing Signals on Narrow Lap Weld [J]. China Mechanical Engineering, 2019, 30(02): 225-229.
[4]	ZENG Qiang, DENG Jingyuan, CHANG Menghui, ZHANG Jinchun. Genetic Evalution Method for JSP under Mixed Work Calendars [J]. China Mechanical Engineering, 2018, 29(22): 2690-2702.
[5]	QUAN Lingxiao1, 2;GUO Hai Xin1,3;SHENG Shiwei3;LI Lei1. Fault Diagnosis of Electro Hydraulic Servo Valves Based on GA+LM Algorithm Optimized BP Neural Networks [J]. China Mechanical Engineering, 2018, 29(05): 505-510.
[6]	AN Xingqiang1;GU Zhengqi1,2;MA Xiaokui1;ZHANG Sha1;MI Chengji3. Fatigue Life Prediction Method Based on Weld Quality Parameter Interval Model [J]. China Mechanical Engineering, 2017, 28(21): 2639-2645.
[7]	LIN Xiezhao;WANG Fuzhen. Multi-objective Optimization of Threaded Connection on Tension Rods for a Uniform Load Distribution between Teeth [J]. China Mechanical Engineering, 2017, 28(17): 2024-2028.
[8]	GUO Yingfu1;LUO Yanwang1;ZHAO Yanming2;HE Hongxi1;TAN Huajiang1. Constraint Tolerance Optimization Design Based on Performance Deviation [J]. China Mechanical Engineering, 2017, 28(17): 2069-2074.
[9]	SI Yuan, QIAN Lijun, QIU Lihong, LI Hao. Energy Management of a 4WD Hybrid Electric Vehicle Based on ECMS [J]. China Mechanical Engineering, 2017, 28(09): 1112-1117.
[10]	Zhou Kaijun, Xiao Yi, Zhou Xiaoqing. A Method for CMM Inspection Path Planning Based on GA [J]. China Mechanical Engineering, 2016, 27(12): 1626-1631.
[11]	Cui Baozhen, Ma Zewei , Li Huilong, Wang Shan. Fault Diagnosis of Rolling Bearings Based on Generalized Morphological Filter and Hilbert Marginal Spectrum [J]. China Mechanical Engineering, 2016, 27(11): 1484-1488.
[12]	Chen Yongjiang, Tang Wencheng, Gan Weimin, Meng Haodong. Study on Friction of Precision Ball Screws Based on Creep Force Models [J]. China Mechanical Engineering, 2016, 27(02): 178-186.
[13]	Ji Huawei, Liu Maona, Hu Xiaoping. Optimization of MSMA Actuator Hysteresis Model Based on Hybrid GA [J]. China Mechanical Engineering, 2015, 26(4): 508-512.
[14]	Wu Zhantao, Cheng Junsheng, Yang Yu. Adaptive Characteristic-scale Decomposition Method and Its Applications [J]. China Mechanical Engineering, 2015, 26(23): 3184-3191,3199.
[15]	Yang Yu, Li Zizhu, He Zhiyi, Cheng Junsheng. The Rolling Bearing Fault Diagnosis Method Based on ASTFA De-noising and AKVPMCD [J]. China Mechanical Engineering, 2015, 26(21): 2934-2940.