A Novel Approach to Analyzing the Mechanical Response of Component Failure in Cable Truss Structures Using an Improved Lstm Neural Network

Abstract

In the large cable structure, the cable as the main force component bears a large load. How to accurately analyze the influence of cable rupture on the structure and give reasonable maintenance measures has become the key to health monitoring of large cable structures. In this study, a cable truss structure (CTS) is taken as the research object, and the mechanical response analysis method of component failure using an improved LSTM neural network is proposed. Firstly, the component failure and mechanical response acquisition mechanism are designed according to the CTS experimental model. The measured parameters are used as indicators to evaluate the simulation accuracy. In order to improve the accuracy of finite element simulation, a transient dynamic analysis method is proposed. Based on the simulation model and calculation method, the mechanical response analysis and parameter analysis of typical failure conditions are carried out. The most unfavorable failure mode is obtained, and the best maintenance measures for component failure are given. Considering the time correlation of component failure, a mechanical response prediction method based on CNN-BiLSTM optimized by IPSO is proposed. Combined with finite element simulation data samples and prediction methods, the mapping relationship between component failure and mechanical response is established. Finally, the continuous dynamic analysis of component failure is realized, and the most unfavorable failure path is obtained. The research results show that the accuracy of the established simulation model and calculation method is more than 95%. The improved deep learning prediction model significantly improves the prediction accuracy and efficiency, and the analysis error and time cost are reduced by 7.5% and 23.7%, respectively.

Keywords: cable truss structure, component failure, cable rupture analysis, mechanical response, algorithm improvement