An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions

Abstract

Structural Health Monitoring (SHM) technology is considered to be a key technology to reduce the maintenance cost and meanwhile ensure the operational safety of aircraft structures. It has gradually developed from theoretic and fundamental research to real-world engineering applications in recent decades. The problem of reliable damage monitoring under time-varying conditions is a main issue for the aerospace engineering applications of SHM technology. Among the existing SHM methods, Guided Wave (GW) and piezoelectric sensor-based SHM technique is a promising method due to its high damage sensitivity and long monitoring range. Nevertheless the reliability problem should be addressed. Several methods including environmental parameter compensation, baseline signal dependency reduction and data normalization, have been well studied but limitations remain. This paper proposes a damage propagation monitoring method based on an improved Gaussian Mixture Model (GMM). It can be used on-line without any structural mechanical model and a priori knowledge of damage and time-varying conditions. With this method, a baseline GMM is constructed first based on the GW features obtained under time-varying conditions when the structure under monitoring is in the healthy state. When a new GW feature is obtained during the on-line damage monitoring process, the GMM can be updated by an adaptive migration mechanism including dynamic learning and Gaussian components split-merge. The mixture probability distribution structure of the GMM and the number of Gaussian components can be optimized adaptively. Then an on-line GMM can be obtained. Finally, a best match based Kullback-Leibler (KL) divergence is studied to measure the migration degree between the baseline GMM and the on-line GMM to reveal the weak cumulative changes of the damage propagation mixed in the time-varying influence. A wing spar of an aircraft is used to validate the proposed method. The results indicate that the crack propagation under changing structural boundary conditions can be monitored reliably. The method is not limited by the properties of the structure, and thus it is feasible to be applied to composite structure.

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

“…These services also challenge the traditional structural safety assessment and maintenance guarantee [1][2][3]. The application of structural health monitoring (SHM) technology, which has gradually become a popular research area, can effectively prevent major accidents, achieve the maintenance of conditions, and reduce maintenance costs [4][5][6][7]. 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.…”

Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring

“…For example, traditional multivariate PDFs (such as a multivariate normal distribution) cannot model the PDF with multiple peaks. The multivariate mixture PDFs (such as multivariate normal mixture model), utilized in SHM and damage detection [ 10 , 11 , 12 , 13 ], rely on the proper choice of the number and the type of the mixture distributions and an initial value of parameter vector in optimization [ 14 ]. The Nataf distribution, utilized in SHM and structural reliability [ 15 , 16 ], relies on the assumption that the transformed random variables, obtained from the marginal transformations of the original random variables, are multivariate normal distribution [ 17 ].…”

Active Monitoring of Fatigue Crack in the Weld Zone of Bogie Frames Using Ultrasonic Guided Waves

“…Among these active SHM methods, ultrasonic guided wave-based active SHM technology using a built-in piezoelectric sensor network is being widely investigated and developed for detecting cracks in various types of metallic structures. Much research has been conducted on monitoring fatigue crack growth in metallic structures [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37], including the physics-based damage index, relating sensor measurements to crack growth size [17], the interaction of Lamb wave modes at varying frequencies with a through-thickness crack of different lengths [20], Lamb wave mode decomposition using concentric ring and circular piezoelectric transducers [21], a multi-feature integration method based on a second-order multivariate regression analysis for the prediction of fatigue crack lengths using sensor measurements [24], and a Gaussian mixture model for monitoring crack propagation mixed in the time-varying influence [36].…”