Structural damage identification under variable environmental/operational conditions based on singular spectrum analysis and statistical control chart

2021 ◽  
Author(s):  
Yansong Diao ◽  
Zongzhen Sui ◽  
Kongzheng Guo
Keyword(s):  
Control Chart ◽  
Spectrum Analysis ◽  
Structural Damage ◽  
Damage Identification ◽  
Singular Spectrum Analysis ◽  
Statistical Control ◽  
Operational Conditions ◽  
Singular Spectrum ◽  
Structural Damage Identification

scholarly journals A new approach for structural damage detection exploring the singular spectrum analysis

2016 ◽  
Vol 28 (9) ◽  
pp. 1160-1174 ◽  
Author(s):  
Mario A de Oliveira ◽  
Jozue Vieira Filho ◽  
Vicente Lopes ◽  
Daniel J Inman
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Spectrum Analysis ◽  
Structural Damage ◽  
Singular Spectrum Analysis ◽  
Reconstruction Process ◽  
Structural Health ◽  
Singular Spectrum

This article presents a novel approach for damage detection applied to structural health monitoring systems exploring the residues obtained from singular spectrum analysis. In this technique, a lead zirconate titanate patch acting as actuator excites the structure, and three other patches are used as sensors to receive the structural responses. This method is based on a high-frequency excitation range in order to overcome the problem caused when the low-vibration modes are excited. In this method, a wideband chirp signal, with low amplitude and variable frequency, is used to excite the structure. The response signals are acquired in the time domain, and the singular spectrum analysis procedure is performed. The residues obtained between the reconstructed and original time series are used to compute statistical metrics. The residues calculated from singular spectrum analysis are used to compute the root mean square deviation and correlation coefficient deviation metric indices, rendering the damage detection approach more reliable. Tests were carried out on an aluminum plate, and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for structural health monitoring applications. The results exploring different numbers of components used during the reconstruction process of time series are obtained, and the highlights are presented.

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