A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance. (May 2019)

• Record Type: Journal Article
• Authors: Yuan, Shenfang; Zhang, Jinjin; Chen, Jian; Qiu, Lei; Yang, Weibo
• Is Part Of: Structural health monitoring. Volume 18:Number 3(2019)
• Page Start: 853
• Page End: 868
• Publication Date: 2019-05
• Subjects: Damage evaluation -- guided wave–hidden Markov model -- performance improvement -- uniform initialization -- attachment lug; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921718783652
• Languages: English
• ISSNs: 1475-9217

Abstract

During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.