Structural Health Monitoring Using Improved Subspace Identification Method by Including Rotational Degrees of Freedom

2020 ◽  
pp. 215-226
Author(s):  
Diptojit Datta ◽  
Anjan Dutta
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degrees Of Freedom ◽  
Subspace Identification ◽  
Structural Health ◽  
Identification Method ◽  
Rotational Degrees Of Freedom ◽  
Subspace Identification Method

Machine learning paradigm for structural health monitoring

2020 ◽  
pp. 147592172097241
Author(s):  
Yuequan Bao ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degrees Of Freedom ◽  
Monitoring Data ◽  
Learning Paradigm ◽  
Structural Health ◽  
Diagnosis And Prognosis ◽  
Cable Force ◽  
Health Diagnosis

Structural health diagnosis and prognosis is the goal of structural health monitoring. Vibration-based structural health monitoring methodology has been extensively investigated. However, the conventional vibration–based methods find it difficult to detect damages of actual structures because of a high incompleteness in the monitoring information (the number of sensors is much fewer with respect to the number of degrees of freedom of a structure), intense uncertainties in the structural conditions and monitoring systems, and coupled effects of damage and environmental actions on modal parameters. It is a truth that the performance and conditions of a structure must be embedded in the monitoring data (vehicles, wind, etc.; acceleration, displacement, cable force, strain, images, videos, etc.). Therefore, there is a need to develop completely novel structural health diagnosis and prognosis methodology based on the various monitoring data. Machine learning provides the advanced mathematical frameworks and algorithms that can help discover and model the performance and conditions of a structure through deep mining of monitoring data. Thus, machine learning takes an opportunity to establish novel machine learning paradigm for structural health diagnosis and prognosis theory termed the machine learning paradigm for structural health monitoring. This article sheds light on principles for machine learning paradigm for structural health monitoring with some examples and reviews the existing challenges and open questions in this field.

scholarly journals Load-Identification Method for Flexible Multiple Corrugated Skin Using Spectra Features of FBGs

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 134
Author(s):  
Zhaoyu Zheng ◽  
Jiyun Lu ◽  
Dakai Liang
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Bragg Grating ◽  
Health Monitoring ◽  
Training Data ◽  
Bragg Grating ◽  
Load Identification ◽  
Structural Health ◽  
Fiber Bragg Grating Sensors ◽  
Identification Method ◽  
Load Size

Flexible corrugated skins are ideal structures for morphing wings, and the associated load measurements are of great significance in structural health monitoring. This paper proposes a novel load-identification method for flexible corrugated skins based on improved Fisher discrimination dictionary learning (FDDL). Several fiber Bragg grating sensors are pasted on the skin to monitor the load on multiple corrugated crests. The loads on different crests cause nonuniform strain fields, and these discriminative spectra are recorded and used as training data. The proposed method involves load-positioning and load-size identification. In the load-size-identification stage, a classifier is trained for every corrugated crest. An interleaved block grouping of samples is introduced to enhance the discrimination of dictionaries, and a two-resolution load-size classifier is introduced to improve the performance and resolution of the grouping labels. An adjustable weight is introduced to the FDDL classification scheme to optimize the contribution from different sensors for different load-size classifiers. With the proposed method, the individual loads on eight crests can be identified by two fiber Bragg grating sensors. The positioning accuracy is 100%, and the mean error of the load-size identification is 0.2106 N, which is sufficiently precise for structural health monitoring.

An Experimental Study of Structural Health Monitoring Using Incomplete Measurements

1996 ◽  
Vol 118 (4) ◽  
pp. 543-550 ◽  
Author(s):  
D. C. Zimmerman ◽  
S. W. Smith ◽  
H. M. Kim ◽  
T. J. Bartkowicz
Keyword(s):  
Structural Health Monitoring ◽  
Model Reduction ◽  
Analytical Model ◽  
Health Monitoring ◽  
Experimental Measurement ◽  
Degrees Of Freedom ◽  
Measurement Problem ◽  
Constraint Matrix ◽  
Structural Health ◽  
Incomplete Measurement

In this paper, algorithmic approaches to enhance structural health monitoring capability when faced with incomplete measurements are addressed. The incomplete measurement problem has two aspects: (i) experimental measurement of a lesser number of modes of vibration than that of the analytical model and (ii) experimental measurement of a lesser number of degrees of freedom than that of the analytical model. Studies comparing model reduction, eigenvector expansion, and a hybrid model reduction/eigenvector expansion to address the second contribution are performed using experimental data. These approaches to the incomplete measurement problem are evaluated within the frameworks of multiple-constraint matrix adjustment (both sparsity and nonsparsity preserving algorithms) and minimum rank perturbation theory, which are both applicable for model refinement as well as damage location. Experimental evaluation of the proposed approaches utilize data from the NASA Langley Research Center 8-bay truss and McDonnell Douglas Aerospace 10-bay truss facilities.

Field measurements of typhoon effects on a transmission tower and its modal parameter identification

2020 ◽  
Vol 23 (8) ◽  
pp. 1548-1561
Author(s):  
Hong-Nan Li ◽  
Jin-Xin Wang ◽  
Xing Fu ◽  
Liang Ren ◽  
Qing Zhang
Keyword(s):  
Finite Element ◽  
Health Monitoring ◽  
Element Model ◽  
Subspace Identification ◽  
Transmission Tower ◽  
Stochastic Subspace Identification ◽  
Wind Speeds ◽  
Identification Method ◽  
Transmission Towers ◽  
Subspace Identification Method

Many transmission towers have collapsed under typhoons in recent years, mainly due to the unclear behaviors of their structural properties, introducing many deficiencies in the design process. Therefore, implementing structural health monitoring is of great importance for investigating the structural features of large-span transmission lines. This study develops a stochastic subspace identification method to identify the modal parameters of transmission towers, and a finite element model of a transmission tower-line system is established based on a case in Guangdong Province, China. Moreover, a MATLAB program is written using the stochastic subspace identification method to perform a modal analysis on the wind-induced responses of a transmission tower, and the results are compared with those of the finite element model to verify the program’s reliability. A structural health monitoring system installed on a transmission tower recorded the wind field data around the tower and its vibration responses during Typhoon Khanun. The characteristics of the typhoon wind field and the changes in the acceleration responses under different wind speeds were calculated, and the developed stochastic subspace identification method was used to identify the frequencies and damping ratios of the tower. The results show that the identified frequencies under different wind speeds in the longitudinal and transverse directions remain essentially unchanged, indicating that the monitoring tower was safe and suffered no damage during Typhoon Khanun. The damping ratios of the monitoring tower range from 1% to 4%, where the larger values may be caused by bolt slippage.

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