Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals
Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals
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DOI：
10.1109/tmag.2015.2498119
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发表时间：
2016-04
影响因子：
2.1
通讯作者：
W. Han;Jun Xu;Mengchu Zhou;G. Tian;Ping Wang;X. Shen;Edwin Hou
中科院分区：
文献类型：
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作者：
W. Han;Jun Xu;Mengchu Zhou;G. Tian;Ping Wang;X. Shen;Edwin Hou
从漏磁信号中准确、及时地预测缺陷尺寸需要有效地解决反问题。本文针对这一问题提出了一种新的求逆方法。该方法将布谷鸟搜索(CS)和粒子滤波(PF)相结合，利用径向基函数神经网络和粒子滤波的观测方程作为正演模型，从测量信号中估计缺陷轮廓。作为一种最新的自然启发式优化算法，CS能够解决高维优化问题。作为非线性滤波问题的一种有效估计器，PF被应用于所提出的逆方法，以提高后者对噪声的鲁棒性。该算法同时具有CS和PF的优点，其中CS为PF产生优化的状态序列，而PF处理状态序列并估计期望的轮廓。仿真和实验结果表明，在噪声环境下，该方法明显优于仅基于CS的逆方法。
Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter's robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.