Self-powered piezo-floating-gate sensors for health monitoring of steel plates
Self-powered piezo-floating-gate sensors for health monitoring of steel plates
复制标题
用于钢板健康监测的自供电压电浮门传感器
DOI：
10.1016/j.engstruct.2017.06.063
复制
发表时间：
2017
影响因子：
5.5
通讯作者：
S. Chakrabartty
中科院分区：
文献类型：
--
作者：
Hassene Hasni;A. Alavi;N. Lajnef;Mohamed H. Abdelbarr;S. Masri;S. Chakrabartty
本文提出了一种使用具有可变注入速率的自供电压电浮栅（PFG）传感器进行结构健康监测的新方法。通过在A36薄钢板上进行面内拉伸模式实验研究来验证所提出的方法。板上安装了不同的压电传感器，既可以为传感器提供动力，又可以监测损坏的进展。由于电子注入而导致的传感器浮栅上的电荷变化被认为是损伤指示参数。为了提高损坏检测精度，基于传感器组概念，从每个存储门的累积电压降中提取了多个特征。然后将获得的特征输入支持向量机（SVM）分类器以识别多种损伤状态。开发了优化过程来优化分类器的参数，以提高检测率的准确性。根据结果，该方法对于检测钢板损伤进展的性能令人满意。
This paper presents a new method for structural health monitoring using self-powered piezo-floating-gate (PFG) sensors with variable injection rates. An experimental study was performed on an A36 thin steel plate subjected to an in-plane tension mode to verify the proposed method. Different piezoelectric transducers were mounted on the plate for both empowering the sensor and monitoring the damage progression. The changes of charge on the floating-gates of the sensor due to electron injection were considered as damage indicator parameters. In order to improve the damage detection accuracy, several features were extracted from the cumulative voltage droppage for each memory gate, based on sensor group concept. The obtained features were then fed into a support vector machine (SVM) classifier to identify multiple damage states. An optimization process was developed to optimize the parameters of the classifier in order to increase the detection rate accuracy. Based on the results, the performance of the proposed method is satisfactory for detecting damage progression in steel plates.