基于贝叶斯估计的漏磁缺陷轮廓重构方法研究  

Defect Profile Reconstruction from Magnetic Flux Leakage Signals Based on Bayesian Estimation

作　　者：苑希超[1] 王长龙[1] 王建斌[1] 

机构地区：[1]军械工程学院电气工程系,河北石家庄050003

出　　处：《兵工学报》2012年第1期116-120,共5页Acta Armamentarii

基　　金：河北省自然科学基金项目(E2008001258);军内科研项目

摘　　要：漏磁缺陷轮廓重构是指由检测到的漏磁信号重构缺陷轮廓及参数,是实现漏磁反演的关键。目前常用的反演方法包括神经网络法和优化法,但神经网络法的计算精度受噪声影响严重,优化法计算量大。针对这些问题,提出基于递推贝叶斯估计的漏磁缺陷重构算法。建立缺陷轮廓与漏磁信号的状态空间模型,将反演问题描述为基于状态和观测方程的典型的离散时间跟踪问题,对漏磁信号进行了反演,并在不同信噪比下对神经网络法和所提方法进行了反演效果的比较。结果表明:基于递推贝叶斯估计方法的漏磁信号反演算法精度高,同时对噪声具有鲁棒性,是一种有效可行的漏磁反演新方法。The reconstruction of magnetic flux leakage （MFL） defect profiles means the reconstruction of defect profiles and parameters from MFL inspection signals. It is the key for the inversion of MFL inspec- tion signals. The studies of MFL inversion problem mainly based on neural network and optimization method. But these two methods have certain shortages. The precision of neural networks may be influ- enced by noises, and the optimization method is computational demanding. To overcome these shortages, a reconstruction approach for solving such inversion problems based on Bayesian estimation method is pro- posed. It formulates the inversion problem as a classical discrete-time tracking problem with state and measurement equations. State-space model of defect profile and MFL signals is established, the proposed method is adapted to reconstruct defect profile and the comparison between neural network and proposed method under different SNR. Results indicate that the proposed method has high accuracy and robustness against noise, and it is an effective and feasible approach for solving inverse problems.

关 键 词：电磁学 漏磁检测 轮廓重构 粒子滤波 重采样 

分 类 号：TG115.28[金属学及工艺—物理冶金]