An adaptive guided wave-Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test

2017 ◽  
Vol 16 (5) ◽  
pp. 501-517 ◽  
Author(s):  
Lei Qiu ◽  
Shenfang Yuan ◽  
Christian Boller
Keyword(s):  
Fatigue Test ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Guided Wave ◽  
Full Scale ◽  
Time Varying ◽  
Damage Monitoring

scholarly journals Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures

2021 ◽  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Probability Model ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Guided Wave ◽  
Time Varying ◽  
Damage Evaluation ◽  
Significant Research ◽  
Structural Boundary

Abstract. The Guided Wave (GW) based Structural Health Monitoring (SHM) method is of significant research interest because of its wide monitoring range and high sensitivity. However, there are still many challenges in real engineering applications due to complex time-varying conditions, such as changes in temperature and humidity, random dynamic loads, and structural boundary conditions. In this paper, a Gaussian Mixture Model (GMM) is adopted to deal with these problems. Multi-dimensional GMM (MDGMM) is proposed to model the probability distribution of GW features under time-varying conditions. Furthermore, to measure the migration degree of MDGMM to reveal the crack propagation, research on migration indexes of the probability model is carried out. Finally, the validation in an aircraft fatigue test shows a good performance of the MDGMM.

Gaussian mixture model–based path-synthesis accumulation imaging of guided wave for damage monitoring of aircraft composite structures under temperature variation

2018 ◽  
Vol 18 (1) ◽  
pp. 284-302 ◽  
Author(s):  
Yuanqiang Ren ◽  
Lei Qiu ◽  
Shenfang Yuan ◽  
Fang Fang
Keyword(s):  
Temperature Variation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Composite Structures ◽  
Gaussian Mixture ◽  
Guided Wave ◽  
Time Varying ◽  
Damage Imaging ◽  
Wave Path ◽  
Path Synthesis

With the capabilities of achieving large-scale monitoring, improving signal-to-noise ratio, and obtaining a high localization accuracy and strong fault tolerance, guided wave and piezoelectric sensor network–based damage imaging technique seems to be the key technique to realize damage localization of complex aircraft composite structures. However, aircraft structures usually work under random and complicated time-varying conditions, which may introduce nonnegligible uncertainties in the acquired guided wave signals and mask the subtle changes caused by damage. The current damage imaging methods barely consider this time-varying issue and are unable to reliably locate damage. To increase reliability, a Gaussian mixture model–based guided wave path-synthesis accumulation imaging method is proposed for damage imaging of complex aircraft composite structures under time-varying conditions. The Gaussian mixture model is used to suppress time-varying influence and achieve time-varying-independent damage characterization, based on which the guided wave path-synthesis imaging is conducted to perform the fusion of sensor network information and generate an image. During the monitoring process, a series of images will be generated with damage information accumulated, and the damage will gradually emerge in these images and can be located eventually. The typical time-varying condition, temperature variation, is chosen to verify the feasibility and effectiveness of the proposed method on a stiffened carbon fiber composite plate; the results show good performance of reliable damage imaging and localization within a temperature range from 0°C to 60°C.

scholarly journals Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1283
Author(s):  
Qiuhui Xu ◽  
Shenfang Yuan ◽  
Tianxiang Huang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Fatigue Tests ◽  
Guided Wave ◽  
Monitoring Method ◽  
Notched Specimen ◽  
Small Cracks ◽  
The Relationship

Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accuracy and stability of crack quantification under uncertainties, this paper proposes a multi-dimensional uniform initialization GMM. First, the multi-channel GW features are integrated to increase the accuracy of crack quantification, as GW features from different channels have different sensitivity to cracks. Then, the uniform initialization method is adopted to provide more stable initial parameters in the expectation-maximization algorithm. In addition, the relationship between the probability migration index of GMMs and crack length is calibrated with fatigue tests on prior specimens. Finally, the proposed method is applied for online crack quantification on the notched specimen of an aircraft spar with complex fan-shaped cracks under uncertainty.

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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