Data fusion and residual convolutional auto-encoder based structural damage identification

被引：0
|
作者：
Liu Y. [1 ]
Jiang Y. [1 ]
Wang S. [1 ]
Ma C. [1 ]
机构：
[1] College of Engineering, Ocean University of China, Qingdao
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2023年 / 42卷 / 04期
关键词：
data fusion; Deep-sea mining riser; one dimensional residual convolutional auto-encoder (1D-RCAE); principal component analysis (PCA); structural damage identification;
D O I：
10.13465/j.cnki.jvs.2023.04.023
中图分类号：
学科分类号：
摘要：
Deep-sea mining riser continuously suffer from internal Solid-liquid two-phase fluid abrasion and external wind-wave-current coupling load during operation, and structure damage gradually accumulates. Because of high slenderness ratio and flexibility, it is difficult to identify modal parameters, and the damage sensitivity of single measuring point response is low when applying traditional damage detection method to deep-sea mining riser. To solve the problems, a data fusion and one dimension residual convolutional auto-encoder (1D-RCAE) based method is proposed for deep-sea mining riser damage identification. Firstly, PCA is used to fuse bending strain responses from multiple measuring points into one variable. Then, the 1D-RCAE is used to extract the damage sensitive feature (DSF) from the fused variable. Lastly, the Mahalanobis distance between the extracted DSF under the currently testing and the baseline conditions is selected as the damage index. The damage detection effectiveness is verified on a 500m numerical model and a laboratory model of deep-sea mining riser, and the result shows that the accuracy of damage identification is higher than 98%. At the same time, the effects of noise pollution and changing marine environment is explored. © 2023 Chinese Vibration Engineering Society. All rights reserved.
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页码：194 / 203
页数：9
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