何存富,王志,刘秀成,王学迁,吴斌.基于GA PSO混合算法的钢杆磁特性参数识别方法[J].仪器仪表学报,2017,38(4):838-843
基于GA PSO混合算法的钢杆磁特性参数识别方法
Magnetic property parameter identification of steel pole based on GA PSO hybrid algorithm
  
DOI:
中文关键词:  磁特性曲线  参数识别  遗传算法  粒子群算法  钢杆
英文关键词:magnetic characteristic curve  parameter identification  genetic algorithm  particle swarm optimization  steel pole
基金项目:国家自然科学基金(11527801)项目资助
作者单位
何存富 北京工业大学机械工程与应用电子技术学院北京100124 
王志 北京工业大学机械工程与应用电子技术学院北京100124 
刘秀成 北京工业大学机械工程与应用电子技术学院北京100124 
王学迁 北京工业大学机械工程与应用电子技术学院北京100124 
吴斌 北京工业大学机械工程与应用电子技术学院北京100124 
AuthorInstitution
He Cunfu College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
Wang Zhi College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
Liu Xiucheng College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
Wang Xueqian College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
Wu Bin College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
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中文摘要:
      测量轴类零件的磁滞回线,利用其特征参数的变化表征零件表面硬度及硬化层深度,是具有工程应用前景的电磁无损检测新技术之一,其关键是轴类零件磁特性曲线测量装置的研制和磁特性参数高精度识别方法的研究。设计出一种基于闭环磁路的钢杆磁滞回线测量实验装置,并基于J A磁滞模型,提出了一种遗传粒子群(GA PSO)混合算法,实现了钢杆磁滞回线全局与局部特征参数的快速、高精度识别。实验测得的3种不同材质钢杆磁滞回线,对比分析了混合优化算法与单一算法(遗传、粒子群、模拟退火)的参数识别速度与精度,结果表明,混合算法全局识别结果的最小均方根误差仅为0.004 7,低于单一算法的相应结果;混合算法对局部特征参数(矫顽力、剩余磁感应强度)识别的相对误差均小于0.35%,优于单一算法识别精度。上述实验测试和磁特性参数识别方法,有望应用于销钉、螺栓等轴类构件表面硬化层的无损检测。
英文摘要:
      By measuring the hysteresis loop of shaft parts, the change of its feature can be used to describe the the surface hardness and case depth. It is one of the most promising technologies for nondestructive testing. The key of this technology is to develop the measuring devices and research the high precision identification method. This paper design a hysteresis loop measurement device for shaft parts based on the closed magnetic circuit. A Genetic Algorithm and Particle Swarm Optimization (GA PSO) hybrid algorithm is proposed to identify the parameters based on J A model, which can realize the fast and accurate identification of the global and local characteristic parameters of hysteresis loop. According to the measured hysteresis loops of three different kinds of steel material, the consuming time and accuracy of parameter identification are compared and analyzed among the proposed hybrid algorithm and other algorithms (genetic algorithm; particle swarm optimization; simulated annealing algorithm). The experimental results show that the minimum root mean square error of the global identification results of the hybrid algorithm is only 0.004 7, which is lower than the corresponding results of other algorithms. The relative error of the local feature parameters (coercivity and residual magnetic induction) identification results of the hybrid algorithm is less than 0.35%, which is smaller than other algorithms. The experimental measurements and parameters identification method can be expected to apply for the nondestructive testing for surface hardened layer of shaft component, e.g., dowel pins, bolts.
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