Improving robustness of damage imaging in model-based structural health monitoring of complex structures

2015 ◽  
Vol 138 (3) ◽  
pp. 1766-1766 ◽  
Author(s):  
Patrice Masson ◽  
Nicolas Quaegebeur ◽  
Pierre-Claude Ostiguy ◽  
Peyman Y. Moghadam
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Complex Structures ◽  
Structural Health ◽  
Model Based ◽  
Damage Imaging

Model-based structural health monitoring of naval ship hulls

2011 ◽  
Vol 200 (9-12) ◽  
pp. 1137-1149 ◽  
Author(s):  
Christopher J. Stull ◽  
Christopher J. Earls ◽  
Phaedon-Stelios Koutsourelakis
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Ship Hulls ◽  
Model Based ◽  
Naval Ship

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

2018 ◽  
Vol 18 (2) ◽  
pp. 524-545 ◽  
Author(s):  
Lei Qiu ◽  
Fang Fang ◽  
Shenfang Yuan ◽  
Christian Boller ◽  
Yuanqiang Ren
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Monitoring ◽  
Gaussian Mixture ◽  
Monitoring Method ◽  
Structural Health ◽  
Model Based ◽  
Damage Monitoring

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

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