基于数据融合及残差卷积自编码器的结构损伤识别方法

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Abstract 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.

Key words： Deep-sea mining riser structural damage identification one dimensional residual convolutional auto-encoder (1D-RCAE) principal component analysis (PCA) data fusion

Received: 06 September 2021 Published: 28 February 2023

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	Articles by authors
	LIU Yuchi
	JIANG Yufeng
	WANG Shuqing
	MA Chunke

Cite this article:

LIU Yuchi,JIANG Yufeng,WANG Shuqing, et al. Data fusion and residual convolutional auto-encoder based structural damage identification[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(4): 194-203.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I4/194

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