【作者】 叶波；

【导师】 周泽魁； 黄平捷；

【作者基本信息】 浙江大学 ， 控制科学与工程， 2009， 博士

【摘要】 多层导电结构深层缺陷检测是航空航天、核电等许多重要领域急需解决的关键问题。目前,电涡流检测技术在多层导电结构深层缺陷检测方面的研究及应用大多还处于定性阶段,判断缺陷有无比较成功,而确定缺陷形状、大小和媒质性质的定量化评估因涡流检测反问题的非线性和非适定性,一直是电涡流检测研究的难点和热点。本文结合国家自然科学基金项目,开展了多层导电结构深层缺陷电涡流检测和定量化评估关键技术研究,具有重要的科学意义与应用价值。本文的研究工作和创新点如下:1.针对线圈式电涡流检测方法应用于多层导电结构深层缺陷检测时灵敏度、空间分辨率低、难以检测较深缺陷的实际问题,以场量分析法为基础,应用低频电涡流检测原理,构建了基于GMR磁场传感器的新型电涡流检测平台,为实现多层导电结构深层缺陷的高灵敏度检测和定量化评估打下坚实的基础。2.研究了多层导电结构深层缺陷电涡流检测信号预处理技术。分别研究了时域法、频域法及时频结合法在多层导电结构深层缺陷电涡流检测信号消噪预处理中的应用,提出了两级滤波方式的电涡流检测信号预处理方法,其中第一级采用中值信号跟踪法快速滤除信号中幅值较大的噪声和干扰,第二级采用小波包分析shannon熵准则对信号进行更加精细的去噪处理,进行了检测实验和分析,去噪效果良好。3.研究了多层导电结构深层缺陷电涡流检测信号特征提取技术。针对多层导电结构深层缺陷电涡流检测本身具有较强非线性的特点,提出了基于核主分量分析的多层导电结构深层缺陷电涡流检测信号特征提取方法,实验结果表明,该方法由于蕴含非线性核映射,所提取的特征值较为稳定,区分度好,能取得满意的特征提取效果。4.研究了多层导电结构深层缺陷电涡流检测信号识别和分类方法。针对实际电涡流检测中,制备样品成本高,难以加大样本数量的问题,提出了核主分量分析和支持向量机相结合的多层导电结构深层缺陷电涡流检测信号分类方法,该方法充分发挥支持向量机对小样本数据处理的优势,分类准确率较高,可适用于对不同缺陷进行识别和分类。5.研究了基于回归分析法的多层导电结构深层缺陷定量化评估模型。针对电涡流检测数据中隐含的非线性和自相关性,提出了基于核偏最小二乘回归的多层导电结构深层缺陷定量化评估模型。本文利用该模型对多层导电结构中矩形缺陷进行了评估,实验结果表明,缺陷参数评估的相对误差基本都在±20%以内,泛化能力较强。6.研究了基于贝叶斯网的多层导电结构深层缺陷定量化评估模型。针对电涡流检测中的不确定性和随机性,通过贝叶斯网将概率推理运用于多层导电结构深层缺陷电涡流检测和定量化评估,推导了连续变量节点的贝叶斯网的表示方式及其学习和推理算法,并在此基础上构建了用于多层导电结构深层缺陷定量化评估的贝叶斯网模型,实验结果表明,该模型单独对圆柱形缺陷直径进行评估时相对误差在±9%以内,对圆柱形缺陷直径和深度同时进行评估时相对误差在±11%以内,具有良好的适用性和鲁棒性。更多还原

【Abstract】 As it is known to all, detection and quantitative evaluation of deep defects in multi-layered structures is an essential task in a number of industries ranging from aerospace industry to atomic engineering. It is widely recognized as one of the difficult inverse problems and principal challenges in the research field of eddy current nondestructive testing (ECNDT). Now, for ECNDT, the qualitative study in multi-layered structures has been presented. However, the quantitative evaluation problem in multi-layered structures remains to be dealt with. Supported by the National Natural Science Foundation of China under grant 50505045 (2006-2008), we carry out the research of detection and quantitative evaluation of deep defects in multi-layered structures from eddy current (EC) signals. The obtained results in this paper are important for the continued scientific research and application in this field. The main works and innovations of the paper are as follows:1. The field analysis based ECNDT method is investigated. The experimental ECNDT system based on giant magnetoresistive (GMR) probe for estimating dimensions of deep defects in multilayered structures is developed successfully. Here, a new EC testing technique with a GMR sensor is used to enhance the sensitivity and spatial resolution of the measurement. The GMR based EC probe can perform better than the conventional probe for low-frequency applications, i.e., when detecting defects deep buried in multi-layered structures.2. Signals preprocessing techniques are studied. Signals preprocessing techniques for noise elimination including time-domain methods, frequency-domain methods, and time-frequency domain methods are studied, respectively. We present a general robust procedure for signals preprocessing. Firstly, the original signals are preprocessed by median signal tracking consists of evaluating the distance between consecutive samples in the complex plane for the impulse noise suppression. Then, the wavelet packet analysis method with Shannon entropy threshold is implemented for EC signals de-noising. The experimental results show that the presented method is superior for EC signal de-noising.3. Signal feature extraction techniques are studied. In ECNDT, the interpretation of EC signals which are produced by a pick-up probe is very difficult since the electromagnetic field is nonlinear. We present a novel EC signal feature extraction method by using kernel principal component analysis (KPCA). By benefiting from a kernel perspective, KPCA is more powerful and applicable to nonlinear processing in a simpler and nicer way. It is shown by extensive experiments that KPCA is proper for signal feature extraction in ECNDT.4. The recognition and classification methods of signals from deep defects in multi-layered structures are studied. A novel approach for EC signal classification using the method integrated KPCA and support vector machine (SVM) has been proposed and investigated. SVM can be carried out well in the classification of low amounts of EC signal records due to its remarkable characteristics such as the structural risk minimization principal. Experimental results show that this approach can provide promising classification performance of different types of EC signals.5. The quantitative evaluation models for estimating dimensions of defects in multi-layered structures are studied by using regression algorithms. In the paper, a kernel partial least squares (KPLS) regression method has been proposed to construct the inversion model of defect dimension estimation. It can be used to model nonlinear EC data relations. When the KPLS regression is used to estimate the dimensions of the rectangular defects, the relative errors are less than±20%.6. The quantitative evaluation models for estimating dimensions of defects in multi-layered structures are studied by using Bayesian networks (BNs). In ECNDT, the inherent uncertainty and stochastic nature of inspection need to be dealt with. BNs are applied to quantitatively evaluate the realistic multi-dimensional characteristic parameters of defects by probability inference. In this paper, these ideas in context with continuous variables and dependencies are used. We mainly discuss how to construct BNs from the domain knowledge and the real research data, and how to perform probability inference in BNs. Firstly, we considered a simple problem that only diameter of defects in the multi-layered structures needed to be determined. The relative errors are less than±9%. Secondly, a more complex example was considered. The signals collected from multi-layer samples with defects varying diameter and depth are analyzed. The relative errors are less than±11%.更多还原