Abstract: Composite materials have been widely utilized in aircraft structures such as aircraft wings because of excellent performance. Therefore, recognizing the loads acting on composite materials has a significant influence on the structure design and reliability analysis of aircraft. In this work, a method based on fiber Bragg grating (FBG) sensor and convolutional neural network (CNN) was utilized to recognize the complex multi-point load. The experiment was carried out on a composite cantilever beam and the strain data was collected by sensors placed on the structure. Firstly, the strain data was input into the support vector machine (SVM) algorithm to identify the number of applied loads. Furthermore, the strain data was transformed into a rectangular picture according to the arrangement of the measuring points. After the normalization process, the picture was input into the CNN model to realize the positioning and quantification of the multi-point load. Finally, the results of CNN model were compared with the results of back-propagation neural network (BPNN) and gradient boosting decision tree (GBDT), respectively. In the experiment, the recognition accuracy of the SVM model was 99.584%. For the position of the two-point load, the mean absolute error (MAE) of the CNN model is 0.637 9 mm and 0.576 2 mm. The results indicated that the load identification method based on SVM and CNN is an effective method to identify the number of loads, the position and size of the loads applied. Additionally, the method is capable of providing a new solution for aircraft flight loads measurement.
通讯作者:
* 卿新林,1993年于清华大学获得博士学位,现为厦门大学南强特聘教授、航空航天学院博士研究生导师。主要研究方向为结构健康监测、先进传感技术、飞行器健康管理等,并在领域内取得大量系统性、创新性的研究成果,在Composite Structures、Mechanical Systems and Signal Processing、Structural Health Monitoring、Ultrasonics等国际知名杂志发表学术论文170余篇,发明专利30多项。xinlinqing@xmu.edu.cn
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2023, Vol. 37 
