机器学习在金属磁记忆检测中的应用与展望  

Application and prospects of machine learning on metal magnetic memory testing

作　　者：王慧鹏 李海航 石家龙 董丽虹 王海斗 WANG Huipeng;LI Haihang;SHI Jiaong;DONG Lihong;WANG Haidou(School of Mechanical and Electrical Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;National Key Laboratory for Remanufacturing,Army Academy of Armored Forces,Beijing 100072,China;National Engineering Research Center for Remanufacturing,Army Academy of Armored Forces,Beijing 100072,China)

机构地区：[1]江西理工大学机电工程学院,赣州341000 [2]陆军装甲兵学院再制造技术国家重点实验室,北京100072 [3]陆军装甲兵学院机械产品再制造国家工程研究中心,北京100072

出　　处：《无损检测》2024年第8期89-95,共7页Nondestructive Testing

基　　金：国家自然科学基金(52065026)。

摘　　要：金属磁记忆检测技术(MMMT)能够检测以应力集中为代表的早期损伤,在铁磁性构件损伤检测领域具有广阔的应用前景。由于金属磁记忆检测信号十分微弱并且具有较强的非线性,在实际检测时容易出现漏检或误检。机器学习方法对信号数据集特征分析具有良好的自学习和自适应性能力,较适合金属磁记忆检测信号的分析与处理。对机器学习技术在金属磁记忆检测中的应用进行了综述,讨论了金属磁记忆信号传统特征提取方法的特点,分析了机器学习在金属磁记忆检测中的特征提取、定量识别及剩余寿命预测等方面的研究现状,指出了机器学习在金属磁记忆检测领域的难点问题,并对其发展方向进行了展望。Metal magnetic memory testing(MMMT)can detect early damage represented by stress concentration,and has broad application prospects in the field of damage detection of ferromagnetic components.However,the MMMT signals are very weak and nonlinear,and it is prone to have no warning or false warning applied the MMMT is applied in engineering application.The machine learning method is suitable for data processing and analysis in MMMT for its good self-learning ability and adaptability for characteristic patterns finding.This paper focused on the application of machine learning methods in metal magnetic memory testing.The traditional feature extraction methods of MMMT signals were discussed,and the research status of machine learning methods in feature extraction,quantitative recognition and residual life prediction of metal magnetic memory testing was analyzed.The difficulties and future development direction of machine learning methods in the field of metal magnetic memory testing were discussed.

关 键 词：金属磁记忆检测 机器学习 特征提取 定量识别 寿命预测 

分 类 号：TG115.28[金属学及工艺—物理冶金]