Abstract：A rolling bearing fault diagnosis method based on the morphological fractal dimension and Local Characterist-scale Decomposition is proposed. Firstly, Local Characterist-scale Decomposition is used to decompose the mechanical fault signals into a set of Intrinsic scale components, and then the morphological fractal dimension of Intrinsic scale components which contain the Intrinsic scale component characteristics is calculated. This is obtained as a characteristic parameter to judge the signal fault types. The experimental results that the proposed method based on the morphological fractal dimension and Local Characterist-scale Decomposition can realize different signal states (inner fault, outer race fault, rolling element fault and normal) about the bearing fault and the rolling fault diagnosis effectively.
［1］Logan D, Mathew J. Using the correlation dimension for vibration fault diagnosis of rolling element bearings-I-Basic cincepts ［J］.Mechanical Systems and Signal Processing,1996, 10(3):241-250.
［2］谢平,王欢,杜义浩. 基于EMD和Wigner-Ville分布的机械故障诊断方法研究［J］.计量学报,2010, 31(5):390-394.
［3］臧怀刚,李清志,韩艳龙,等. 基于改进的经验模式分解的滚动轴承故障诊断研究［J］.计量学报,2013,34(2):101-105.
［4］谢平,杨玉昕,江国乾,等. 基于局部均值分解的滚动轴承故障诊断新方法［J］.计量学报,2014,35(1):73-77.
［5］孟宗,顾海燕. 基于盲源分离去噪和HHT的旋转机械故障诊断研究［J］. 计量学报,2013,34(3):242-246.
［6］李兵, 张培林, 任国全,等. 基于数学形态学的分形维数计算及在轴承故障诊断中的应用［J］.振动与冲击,2010,29(5): 191-194.
［7］胥永刚, 何正嘉. 分形维数和近似熵用于度量信号复杂性的比较研究［J］.振动与冲击,2003, 22(3):25-27.
［8］张亢,程军圣,杨宇. 基于局部均值分解与形态学分形维数的滚动轴承故障诊断方法［J］. 振动与冲击,2013, 32(9): 91-93.
［9］Yang N X, Qi D W. Blockboard defect detection based on wavelet transform and mathematical morphology［J］.Advances in Intelligent and Soft Computing, 2011,106(3): 455-460.
［10］程军圣,郑近德,杨宇. 一种新的非平稳信号分析方法—局部特征尺度分解法［J］.振动工程学报,2012,25(2):215-219.
2016, Vol. 37 
