鲁棒独立分量分析在结构损伤特征提取中的应用
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摘要
为了实现土木结构损伤模态频率的精确提取,论文将一种新型ICA算法——鲁棒独立分量分析(RobustICA)应用于IASC-ASCE的四层钢结构框架比例模型,选取无损伤和东侧所有斜支撑断裂这两种工况进行模态固有频率提取。首先选取距离激励点较近的检测节点;其次利用RobustICA算法对这些检测节点采集的混合信号进行盲源分离,得到各个独立分量;之后通过频谱分析,确定了不同工况下的模态固有频率。结果显示RobustICA算法分离出的各分量之间的独立性更高,且固有频率能较精确地分离出来。
A new ICA algorithm Robust Independent Component Analysis(RobustICA) is applied to the four-story steelframe scale model of the IASC-ASCE to extract modal natural frequencies in order to extract the damage modal frequencies of civilstructures accurately. Firstly,the detection nodes near the excitation points are selected,then the independent components are obtained by blind source separation using RobustICA algorithm,and then the modal natural frequencies under different conditions aredetermined by spectrum analysis. The results show that the independence among the components separated by RobustICA algorithmis higher and the natural frequencies can be separated more precisely.
A new ICA algorithm Robust Independent Component Analysis(RobustICA) is applied to the four-story steelframe scale model of the IASC-ASCE to extract modal natural frequencies in order to extract the damage modal frequencies of civilstructures accurately. Firstly,the detection nodes near the excitation points are selected,then the independent components are obtained by blind source separation using RobustICA algorithm,and then the modal natural frequencies under different conditions aredetermined by spectrum analysis. The results show that the independence among the components separated by RobustICA algorithmis higher and the natural frequencies can be separated more precisely.
引文
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[2] Saif Nalband,Amalin Prince,Anita Agrawal. Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empiricalmode decomposition with adaptive noise[J]. IET Science,Measurement&Technology,2018,12(3):350-359.
[3]Zhibin Pan,Xiuquan Wu,Zhengyi Li. Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification[J]. IEEE Signal Processing Letters,2017,24(6):828-832.
[4]Su Yang,Farzin Deravi. On the Usability of Electroencephalographic Signals for Biometric Recognition:A Survey[J]. IEEE Transactions on Human-Machine Systens,2017,47(6):958-969.
[5]Dong Yu,Jinyu Li. Recent Progresses in Deep LearningBased Acoustic Models[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(3):396-409.
[6]P. Comon. Independent component analysis,a new concept?[Z]. Signal Processing,1994,36(3):287-314.
[7]Xi Zhao,Honggeng Yang. A New Method to Calculate theUtility Harmonic Impedance Based on FastICA[J]. IEEETransactions on Power Delivery,2016,31(1):381-388.
[8]Ganesh R. Naik,S. Easter Selvan and Hung T. Nguyen.Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for DiagnosingNeuromuscular Disorders[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2016,24(7):734-743.
[9]Jinlin Zhu,Zhiqiang Ge,Zhihuan Song. Non-Gaussian Industrial Process Monitoring With Probabilistic Independent Component Analysis[J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(2):1309-1319.
[10]Daniel G. Silva,Romis Attux. Simulated Annealing for Independent Component Analysis Over Galois Fields[J].IEEE Signal Processing Letters,2018,25(4):516-520.
[11] Yongchao Yang,Satish Nagarajaiah. Time-FrequencyBlind Source Separation Using Independent ComponentAnalysis for Output-Only Modal Identification of HighlyDamped Structures[J]. Journal of Structural Engineering,2013,139(10):1780-1793.
[12]Maoqi Chen,Ping Zhou. A Novel Framework Based onFastICA for High Density Surface EMG Decomposition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2016,24(1):117-127.
[13]Shahab Basiri,Esa Ollila and Visa Koivunen. AlternativeDerivation of FastICA With Novel Power Iteration Algorithm[J]. IEEE Signal Processing Letters,2017,24(9):1378-1382.
[14] Vicente Zarzoso,Pierre Comon. Robust IndependentComponent Analysis by Iterative Maximization of theKurtosis Contrast With Algebraic Optimal Step Size[J].IEEE Transactions on Neural Networks,2010,21(2):248-261.
[15]Johnson E. A.,Lam H. F. and Katafygiotis L. S. Phase IIASC-ASCE structural health monitoring benchmarkproblem using simulated data[J]. J. Eng. Mech.,2004,130(1):3-15.
[16]J. Ching,J. L. Beck. Bayesian Analysis of the Phase IIIASC-ASCE Structural Health Monitoring ExperimentalBenchmark Data[J]. J. Eng. Mech.,2004,130(10):1233-1244.