摘要
不平衡是造成转子系统振动过大、影响其安全运行的重要因素。传统的最小二乘算法(least squares,简称LS)在不平衡量识别过程中存在对外界干扰或异常值敏感的问题,改进的加权最小二乘算法(weighted least squares,简称WLS)虽然能够降低异常值的影响,但需要经验积累并对振动数据进行深入分析。提出一种基于稳健回归分析的转子系统不平衡量识别方法,通过构建优化的目标函数自动消除异常值的影响,得到正常状态下转子系统不平衡量的最佳估值。实验结果表明,该方法能够有效消除外界干扰和异常值的影响,准确识别出转子系统不平衡量。
Rotor mass unbalance is the most common cause of excessive vibration in rotating machinery and can affect the work performance and safety operations of the system.The traditional least squares algorithm is sensitive to outliers in the process of estimating rotor system unbalance.By manually assigning different weight coefficients,the weighted least squares algorithm can reduce the impact of outliers,but requires practical experience and have deep analysis of the vibration data.In view of these problems,a new unbalance estimation method based on robust regression analysis is proposed.The effect of outliers is automatically eliminated by an optimized objective function.Experimental results show that the proposed method can effectively reduce the influence of outliers and more accurately identify the unbalance of the rotor system.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2016年第1期126-130,202,共5页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51365051)
教育部新世纪优秀人才计划资助项目(NCET-13-0461)
中国博士后科学基金资助项目(2012M521758)
学校博士学科点专项科研基金资助项目(20120201120043)
基本科研业务经费资助项目
关键词
稳健回归分析
转子
最小二乘法
不平衡
robust regression analysis
rotor
least squares algorithm
unbalance
