Improved density peak clustering-based adaptive Gaussian mixture model for damage monitoring in aircraft structures under time-varying conditions

Abstract

Abstract For the reliable damage monitoring of aircraft structures under Time-Varying Conditions (TVCs), Gaussian Mixture Model (GMM)-based damage monitoring methods combined with Guided Wave (GW) have been studied increasingly frequently in recent years. In this paper, to enhance the performance of the GMM-based damage monitoring method, an Improved Density Peaks Clustering (IDPC)-based Expectation Maximization (EM) algorithm is proposed to improve the constructing algorithm of the GMM. In the algorithm, the unique initial value of the GMM parameters is obtained based on an adaptive searching strategy of the probability density peaks of GW Damage Features (DFs) first. Next, the GMM is constructed simply and efficiently by the EM algorithm based on the initial parameters. The IDPC-EM algorithm can stably construct the GMM with fewer experience-dependent initialization parameters while promising the probability modelling accuracy and maintaining high computational efficiency. Based on the IDPC-EM algorithm, a new adaptive GMM-based damage monitoring method combined with the GW is established. The method is simple, stable, highly computationally efficient and less experience-dependent due to the adaptive GMM. In this method, two GMMs of the DFs obtained in the healthy state and the damage monitoring state of an aircraft structure are constructed, respectively. By measuring the similarity of the two GMMs, the variation trend of the probability distributions of the DFs between the two states is obtained to detect the damage. Finally, the method is validated in a full-scale aircraft fatigue test which is close to an in-flight load test on the ground. The cracks of the right landing gear spar and the left wing panel are monitored reliably and stably under random fatigue load conditions.