基于可变激励条件下多维磁信号的机械零部件状态检测方法

项目名称： 基于可变激励条件下多维磁信号的机械零部件状态检测方法

项目编号： No.51275048

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 机械、仪表工业

项目作者： 张卫民

作者单位： 北京理工大学

项目金额： 75万元

中文摘要： 机械零部件状态和设备性能、寿命密切相关。研究能够应用于生产和使用现场的零件状态快速检测技术，是机械制造领域十分重要而又未能很好解决的问题。各种激励条件下的铁磁材料表面磁信号，含有残余应力、缺陷等丰富的被测零件状态信息；且磁无损检测方法适于快速检测和评定铁磁性材料应力和微缺陷状态。针对磁记忆等检测方法存在的问题，拟开展改变外激励磁场条件下的多维磁信号检测技术研究，其特点是不显著增加传感器数量，通过改变激励条件，即可获得零件状态的丰富信息。工作内容包括设计研制可变激励的多维磁信号检测装置；进行不同激励条件下磁信号的检测实验；进行多信号融合和解耦算法研究；研究在地磁场和不同强度的交、直流磁化条件下的磁感应输出信号特征，探求建立残余应力、微缺陷等状态量和磁感应多维信号的定量关系。通过系统性试验，确定检测不同材料的最佳磁化条件组合；开展材料学微观实验和铁磁学机理研究，努力探索磁检测领域新方法。

中文关键词： 可变磁激励；机械零部件；多维磁信号；金属磁记忆；状态检测

英文摘要： The performance and life of equipment is closely related to the state of ferromagnetic mechanical components. A fast detecting technology to ensure the production of machinery manufacturing is very important but not well developed. In different exciting conditions, magnetic signals on the ferromagnetic material surface contents different residual stress, defect and other information; On the other hand, the magnetic nondestructive testing method can be well used in the rapid detection and assessment of the stress and micro defect state of ferromagnetic material. A new detecting method under multidimensional magnetic excitation field will be carried out to solve the problems of magnetic memory testing method. The characteristics of this study will be not significantly increase the number of sensors, but also getting abundant information of part state by changing the exciting conditions. Further work will be done to study the behaviors of ferromagnetic samples based on the former research, a magnetic detecting device with variable excitation will be designed and developed, and different magnetic signal detecting experiments under different exciting conditions will be conducted; Then, signals will be analyzed with multi-fusion and decoupling algorithm technology. finally,the relationship between the three-dimension

英文关键词： Variable magnetic excitation；Mechanical parts；Multidimensional magnetic signal；Metal magnetic memory；State detection