An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network. (18th August 2022)

• Record Type: Journal Article
• Authors: Xing, Tianye; Wang, Yidan; Liu, Yingxue; Wu, Qi; Ma, Rong; Shang, Xiaoling
• Other Names: Liu Ye Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2022(2022)
• Publication Date: 2022-08-18
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2022/4757620
• Languages: English
• ISSNs: 1176-2322

Abstract

Abstract : An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment . The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.