On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring

Chen, Jian; Yuan, Shenfang; Jin, Xin

doi:10.1016/j.ymssp.2019.05.022

Cited by 57 publications

(36 citation statements)

References 36 publications

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“…The PDF of the baseline GMM and the nth monitoring GMM are Ф 0 and Ф n , respectively. The KL divergence-based PMI can be calculated by Equation (13).…”

Section: On-line Migration Measuring Methods Of the Mdui-gmmmentioning

confidence: 99%

“…That is, the PDF of monitoring GMM undergoes a cumulative migration change compared to the PDF of the baseline GMM. The PMI between monitoring GMM and baseline GMM can be calculated by Equation (13). In combination with the measured crack length, a calibration can be performed, which will be discussed in Section 3.4.…”

Section: The Construction Process Of Mdui-gmmmentioning

confidence: 99%

“…However, there are some obstacles to crack monitoring in real engineering applications. First, initiation and growth of crack are affected by various uncertainties [ 12 , 13 ], such as inhomogeneity of material, scatter of mechanical properties of the material, the randomness of cracking process, and quality of manufacturing, etc. These uncertain factors lead to the scatter of both crack size and fatigue lifetime.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Huang

2021

Sensors

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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.

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“…The PDF of the baseline GMM and the nth monitoring GMM are Ф 0 and Ф n , respectively. The KL divergence-based PMI can be calculated by Equation (13).…”

Section: On-line Migration Measuring Methods Of the Mdui-gmmmentioning

confidence: 99%

Section: The Construction Process Of Mdui-gmmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yuan

Huang

2021

Sensors

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“…These damage detection results are always contaminated with uncertainties, such as measurement noise, environment condition, simplifications, and errors in computational models. 14–17 Thus, the results can deviate significantly from the real situation. Besides, due to the limited measurement and noise, damage parameter identification is often an ill-posed or ill-conditioned inverse problem.…”

Section: Introductionmentioning

confidence: 99%

IWSHM 2019: Perturbation-based Bayesian damage identification using responses at vibration nodes

Huang

Schröder

2021

Structural Health Monitoring

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One important topic for structural health monitoring is to achieve accurate damage detection with a small number of noisy sensors and without the requirement of a high-fidelity finite element model. This article adopts the Bayesian probabilistic approach combined with a perturbation model using responses at a few vibration nodes for damage monitoring. First, the node displacement, or the response at vibration node, is adopted in this study for real-time damage assessment with a relatively small number of sensors. Then, the construction method of the node displacement response curves based on the perturbation model is proposed to replace the expensive finite element model. After that, a Bayesian framework integrated the node displacement measurement and the response curves are adopted to acquire the probability distribution of the damage parameter. In this article, the accuracy of the node displacement–based Bayesian framework with the perturbation method is evaluated. The proposed method is applied to a supporting structure of a sailplane under different noise levels.

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“…Among the SHM methods, the elastic-wave-based SHM method is regarded as a promising damage monitoring method because it is sensitive to metal cracks and the impact damage of composite materials, and can be used to monitor structures in real time. It is considered to be a promising damage monitoring method [8][9][10][11][12][13][14][15]. Piezoelectric sensors are commonly used to excite and receive elastic waves in structures, and thus, damage monitoring in structures can be combined with the damage imaging method [16].…”

Section: Introductionmentioning

confidence: 99%

Sector Piezoelectric Sensor Array Transmitter Beamforming MUSIC Algorithm Based Structure Damage Imaging Method

Wang

Qiu

et al. 2020

Sensors

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Elastic-wave-based structural health monitoring technology has a broad application potential for its sensitivity and ability to achieve regional monitoring. For structures with large damping and specific shapes, the traditional damage monitoring method is limited by the sensor arrangement area and affected by low signal-to-noise ratios, so it is difficult to accurately locate the damage in a structure. To solve this problem, this paper proposed a damage monitoring method based on a sector piezoelectric sensor array for multiple signal classification algorithm. By arranging two sector piezoelectric sensor arrays that are suitable for a specific structure, the damage scattering array signal under the multi-excitation source was obtained and synthesized, the signal-to-noise ratios were improved, and the damage location accuracy was thus improved. The effectiveness of the method was verified by monitoring the damage in a circular bonded structure with a metal ring. Compared with the damage localization methods based on the traditional single excitation source multiple signal classification algorithm, path imaging and delay-sum imaging, this method can achieve better damage location and has a higher localization accuracy.

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On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring

Cited by 57 publications

References 36 publications

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

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

IWSHM 2019: Perturbation-based Bayesian damage identification using responses at vibration nodes

Sector Piezoelectric Sensor Array Transmitter Beamforming MUSIC Algorithm Based Structure Damage Imaging Method

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