基于分段结构局部特征尺度分解的轴承内圈故障识别
Fault Identification of Bearing Inner Ring Based on Local Feature Scale Decomposition of Segmented Structure
皮杨勇1
作者信息
- 1. 内江职业技术学院智能制造与汽车学院,四川 内江 641100
- 折叠
摘要
为了提高轴承振动信号特征提取能力,设计了一种分段结构局部特征尺度分解(PPLCD)方法.根据峭度与相关系数的指标性能,构建了一种峭度-相关系数(K-C)权重评价指标.选择CWRU轴承参数作为测试对象,搭建测试平台.研究结果表明:内圈信号故障分析发现,以PPLCD分解获得的有效分量达到了更小MAE与RMSE,同时形成了更大的相关系数,该结果与外圈故障相同.采用PPLCD分解的方法可以获得比初始LCD更优的性能.该研究可以拓展到其他的机械传动领域,具有很大的推广价值.
Abstract
In order to improve the feature extraction ability of bearing vibration signals,a segmented structure local feature scale decomposition(PPLCD)method is designed.According to the performance of kurtosis and correlation coefficient,a kurtosis correlation coefficient(K-C)weight evaluation index is constructed.CWRU bearing parameters are selected as test objects,and a test platform is built.The results show that the effective components obtained by PPLCD decomposition reach smaller MAE and RMSE,and at the same time form a larger correlation coefficient,which is the same as that of the outer ring fault analysis.The PPLCD decomposition method can obtain better performance than the initial LCD.The research can be extended to other fields of mechanical transmission and has great popularization value.
关键词
轴承故障/特征提取/故障诊断/分段结构Key words
bearing fault/feature extraction/fault diagnosis/segmented structure引用本文复制引用
出版年
2024
NETL
NSTL
万方数据