Composite Skin/Stringer Panel Damage Detection Based on Modal Strain Energy and Neural Network Technique

Abstract

This paper proposed a new method to detect the damage of composite skin/stringer panel structure using modal strain energy combined with neural network. The change ratio of element modal strain energy is choosen as damage indicator because of it’s highly sensitivity to the location and severity of structure damage. Neural network here play the role of a tool to indentity the damage according to the change ratio of modal strain energy. To achive this, a three layers neural network model is built and the BP arithmetic is used. The proposed method is validated using a numerical simulation of a composite skin/stringer panel with damages in some elements of its FEM mode, which are simulated by reducing elements’ material stiffness properties. The result shows that, this method is robust, accurate and highly efficient with the maximal error limited in 10%.

Application of sensor technologies for local and distributed structural health monitoring

“…SGs are also often used for these purposes. These methods are widely adopted in both civil and aerospace engineering for structure monitoring, sometimes involving the usage of numerical models (FEM) and machine learning algorithms (artificial neural networks (ANNs)) as a statistical model for feature classification, as reported in . A vibration‐based damage diagnosis based on mode shape analysis was presented in , where quantification and localization of damage were performed on a simple beam‐like structure, combining FEM with ANNs and also providing an experimental validation.…”