摘要
针对风电机组滚动轴承振动信号通常具有非线性和低信噪比的特点,提出一种基于形态学多重分形(morphological multi-fractal,简称MMF)和改进的灰色关联分析(improved grey relational analysis,简称IGRA)的滚动轴承故障诊断方法。首先,通过信号质量指数研究了轴承振动信号的多重分形特性;其次,利用形态学方法计算轴承各种状态广义维数与多重分形谱的参数,并分析了各个参数对轴承运行状态的反映能力,选取能够有效区分轴承状态的参数作为故障特征量;然后,引入离差最大化加权对经典的灰色关联模型进行改进,提升了信息的利用率以及模型的可靠性;最后,利用改进的灰色关联分析实现了滚动轴承的故障诊断。通过仿真分析和应用实例对该方法的有效性进行验证,结果表明该方法能准确识别轴承故障类型,较传统方法准确率更高,运算时间更短,适合解决实际工程问题。
This paper presents a fault diagnosis scheme based on morphological multi-fractal(MMF)analysis and improved grey relational analysis(IGRA)for rolling element bearings.In this scheme,firstly,the multifractal characteristics of bearing signals are illustrated by quality index and partition function.Secondly,the parameters of generalized fractal dimension and multi-fractal spectrum in different bearing operating conditions are calculated by morphology,from which some parameters with good discrimination ability are selected as fault-related feature.Thirdly,maximizing deviation is employed to improve the reliability of the classical grey relational analysis.Finally,the effectiveness of this method is verified by simulation analysis and application example.The results show that the proposed scheme can recognize the different fault categories,which is more stable and higher accurate than the traditional method,and the operation time is shorter,which is suitable for solving practical engineering problems.
作者
樊佶
齐咏生
高学金
刘利强
李永亭
FAN Ji;QI Yongsheng;GAO Xuejin;LIU Liqiang;LI Yongting(Institute of Electric Power,Inner Mongolia University of Technology Hohhot,010080,China;Inner Mongolia Key Laboratory of Electrical&Mechanical Control Hohhot,010051,China;Faculty of Information,Beijing University of Technology Beijing,100124,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2021年第6期1081-1089,1234,1235,共11页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(61763037)
内蒙古自治区科技计划资助项目(2019,2020GG0283)
内蒙古自然科学基金面上资助项目(2020MS05029)。
关键词
故障诊断
特征提取
数学形态学
多重分形
离差最大化
灰色关联分析
fault diagnosis
feature extraction
mathematical morphology
multifractal
maximizing deviation
grey relational analysis
