标题:
基于LMD和逻辑回归的滚动轴承故障状态类型识别Roller Bearing Fault Type Identification Based on LMD and Logistic Regression
作者:
王晶晶, 魏永合, 冯睿智, 魏超
关键字:
局部均值分解(LMD), 逻辑回归, 滚动轴承, 遗传算法(GA), 故障状态识别Local Mean Decomposition (LMD), Logistic Regression, Roller Bearing, CA, Fault Type Identification
期刊名称:
《Mechanical Engineering and Technology》, Vol.5 No.2, 2016-06-30
摘要:
针对滚动轴承的故障振动信号的非线性非平稳性，提出了一种基于局部均值分解(Local Mean Decomposition, LMD)方法和逻辑回归(LR)的滚动轴承故障诊断方法。该方法将采集到的滚动轴承内圈、外圈振动信号进行LMD方法处理后，采用遗传算法(GA)和逻辑回归结合进行模型中的参数选择，通过逻辑回归进行训练和测试，结果表明该方法可以有效地对滚动轴承故障类型进行识别。
Aiming at the nonlinear and non-stationary vibration signal of the rolling bearing, a method based on local mean decomposition (Local Mean Decomposition, LMD) and logistic regression is proposed. This method processed collected vibration signals of rolling bearing inner ring and outer ring by LMD method, then selected the parameter of the model by genetic algorithm (GA) combined with logistic regression, and finally trained and tested the parameter by logistic regression. The result shows that the method can be effectively applied in roller bearing fault type identification.Aiming at the nonlinear and non-stationary vibration signal of the rolling bearing, a method based on local mean decomposition (Local Mean Decomposition, LMD) and logistic regression is proposed. This method processed collected vibration signals of rolling bearing inner ring and outer ring by LMD method, then selected the parameter of the model by genetic algorithm (GA) combined with logistic regression, and finally trained and tested the parameter by logistic regression. The result shows that the method can be effectively applied in roller bearing fault type identification.