Kim Ju-Won, Park Seunghee
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea.
Sensors (Basel). 2018 Jan 2;18(1):109. doi: 10.3390/s18010109.
In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.
在本研究中，磁通泄漏（MFL）方法被用于检测钢丝绳，该方法是一种适用于连续铁磁结构的无损检测（NDE）方法。为了通过实验证明所提出的损伤检测方法，使用霍尔传感器阵列和磁轭制作了一个多通道MFL传感器头，以适应钢丝绳。为了制备受损钢丝绳试样，在钢丝绳上施加了几种不同程度的人工损伤。使用MFL传感器头扫描受损试样以测量磁通信号。获取信号后，进行了一系列信号处理步骤，包括基于希尔伯特变换（HT）的包络处理，以通过减少意外噪声更好地识别MFL信号。然后通过将包络信号与基于广义极值（GEV）分布建立的阈值进行比较，对其进行客观损伤检测分析。通过从MFL信号中提取磁特征，对超过阈值的检测到的MFL信号进行定量分析。为了改进定量分析，还利用了基于包络MFL信号与阈值之间关系的损伤指数以及MFL方法的通用损伤指数。使用所提出的损伤指数和MFL方法的通用损伤指数对每种损伤类型的检测到的MFL信号进行量化。最后，实施了一种基于人工神经网络（ANN）的多阶段模式识别方法，该方法使用提取的多尺度损伤指数来自动估计损伤的严重程度。为了分析基于MFL的钢丝绳自动无损检测方法的可靠性，通过比较反复估计的损伤尺寸和实际损伤尺寸来评估准确性和可靠性。
