Development of a metal magnetic memory method

Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method

“…Self-magnetic flux leakage (SMFL) is assumed to take place in the stress concentration areas of ferromagnetic materials affected by mechanical load under the Earth's magnetic field [46], and this condition can remain even after removing the load, creating detectable magnetic leakage at the material surface [47]. Measuring SMFL at the surface of the materials helps in estimating their stress-strain states (SSSs), which is an important parameter in determining a structure's reliability [48]. Therefore, the relation between localized stress and oriented magnetic domains is useful for detecting defects in ferromagnetic materials within the background magnetic field of the Earth [49].…”

Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique

“…The features of zero-crossing point and gradient value are two key characteristic parameters in MMM testing technique. Experts from Energodiagnostika Co., Ltd., have proposed supplementary rules at the MMM application conference in Anshan in 2004, which is mainly about the comprehensive localization of stress concentration zone through maximum gradient area and zero-crossing point area [1][2][3][4]. Though it is easy to locate stress concentration zone according to the smooth MMM curves like the ones in Figure 1, the actual curves collected are obviously much more complicated than what is in Figure 1, because noise variation is often included when differential derivative technique is employed, which makes it hard to extract the accurate gradient value of signal saltation.…”

“…Since an applied stress can alter the domain structure and have a substantial effect on the low-field magnetic properties, such as remanence and permeability, recently these effects are mostly found in practical applications of magnetic nondestructive testing, actuators, and magnetic sensors. As a result, the effects have been paid considerable attention in the literature [1][2][3][4]. Nevertheless, the coupling effect between mechanical and magnetic properties is so complicated to stunt the development of these properties in nondestructive testing application.…”

“…It has been widely applied in the field of electric power, railway, and petrochemical industry and has been proved effective. Despite of all these, MMM is found to be a natural space domain signal and have the same order of magnitude as the earth magnetic field, which means it is of random nonsmooth signal as well as low signal-to-noise ratio and is vulnerable to noise interference [4]. Meanwhile, quantitative evaluation is affected because it is difficult to correctly extract the features of MMM signal under the hostile subsurface environment, such as high temperature, high pressure, and great noise.…”

Quantitative MMM Characteristic Parameter Prediction for Weld Hidden Damage Status Based on the Fuzzy Weighted Markov Chain

“…首先提出了金属磁记忆检测技术， 是 21 世纪最有应 用前景的诊断技术之一 [1][2] 。 磁记忆检测技术不仅能 都检测铁磁构件的宏观缺陷，而且还能够及时发现 早期应力集中和隐性缺陷，因此在无损检测领域显 示其独特的优势 [3] 。陈海龙等 [4] 将磁梯度张量测量 方法应用到磁记忆检测中，对缺陷磁梯度张量信号 特征进行研究；任吉林等 [5] 率先利用李萨如图的面 积对磁记忆二维检测进行定量分析；刘斌等 [6] 采用 模守恒赝势算法建立全电子势磁力学模型，计算了 固体屈服时，原子磁矩、晶格结构及磁记忆信号的 变化特征；黄海鸿等 [7] 利用疲劳断裂理论建立裂纹 尖端的磁记忆特征信号与应力强度因子之间的关 系；邸新杰等 [8] 提出金属磁记忆信号的二维谱熵分 析方法，可以诊断铁磁材料内部应力集中程度；胥 永刚等 [9] 提出基于固有时间尺度分解的磁记忆特征 提取方法，研究低速重载齿轮故障的早期检测；王 进等 [10] 对磁记忆信号进行小波包分解和小波包能 量谱特征分析；徐坤山等 [11] 研究了焊接残余应力和 焊接缺陷导致磁记忆信号变化规律，结果表明焊接 缺陷导致的磁场梯度的突变远大于焊接残余应力引 起的磁场梯度变化；张卫民等 [12] 提出了通过强化磁 激励场提高磁记忆检测灵敏度和可靠性的方法；邢 海燕等 [13] 提出焊缝检测正交法，对焊缝隐性损伤出 …”