预言
结构健康监测
保险丝(电气)
声发射
结构工程
隐马尔可夫模型
特征(语言学)
传感器融合
可靠性工程
计算机科学
复合数
原位
模式识别(心理学)
数据挖掘
材料科学
工程类
人工智能
复合材料
算法
哲学
气象学
物理
电气工程
语言学
作者
Nick Eleftheroglou,Dimitrios Zarouchas,Θεόδωρος Λούτας,René Alderliesten,Rinze Benedictus
标识
DOI:10.1016/j.ress.2018.04.031
摘要
A novel framework to fuse structural health monitoring (SHM) data from different in-situ monitoring techniques is proposed aiming to develop a hyper-feature towards more effective prognostics. A state-of-the-art Non-Homogenous Hidden Semi Markov Model (NHHSMM) is utilized to model the damage accumulation of composite structures, subjected to fatigue loading, and estimate the remaining useful life (RUL) using conventional as well as fused SHM data. Acoustic Emission (AE) and Digital Image Correlation (DIC) are the selected in-situ SHM techniques. The proposed methodology is applied to open hole carbon/epoxy specimens under fatigue loading. RUL estimations utilizing features extracted from each SHM technique and after data fusion are compared, via established and newly proposed prognostic performance metrics.
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