Abstract
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.
Highlights
Structural health monitoring (SHM) technology has gradually developed from basic theoretical research in the laboratory to real aircraft applications recently [1,2,3,4]
The guided wave (GW) based SHM method has been widely studied because this method can cover a wide monitoring range and it is sensitive to small damage [5,6,7,8,9,10,11]
The joint probability density function (PDF) of the GW feature sample set can be approximately modeled by a Gaussian mixture model (GMM) which is considered as a finite weighted sum of Gaussian components (GCs)
Summary
Structural health monitoring (SHM) technology has gradually developed from basic theoretical research in the laboratory to real aircraft applications recently [1,2,3,4]. The uncertain and random variations of the GW features caused by the time-varying conditions lead to lower reliability and stability for crack monitoring in real engineering applications Considering these uncertainties, some probability statistical models are widely studied for fatigue crack diagnosis and prognosis recently. Yuan and Qiu et al [19] proposed the GW-GMM based method to model the probability characteristic of GW features under time-varying conditions This method is validated in the full-scale aircraft fatigue test. This paper proposes a multi-dimensional uniform initialization Gaussian mixture model (MdUI-GMM) to improve the accuracy and stability of on-line crack quantification under uncertainty.
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