中国机械工程2017，Vol.28Issue(5)：550-558,9.DOI:10.3969/j.issn.1004-132X.2017.05.008
基于自组织神经网络的滚动轴承状态评估方法
Condition Assessment for Rolling Bearings Based on SOM
摘要
Abstract
Aiming at the problems that single feature fault diagnosis accuracy was not too high,a rolling bearing condition assessment method was proposed based on SOM herein.Firstly,the multi-dimensional features were extracted from the original vibration signals and preprocessed by PCA,a fusion model was established by training SOM network and weight vectors of competitive neuron were obtained.Secondly,the fusion index,which was the minimum Euclidean distance between every sam-ple values to the competitive neuron weighting vector,was achieved.Finally,the conditions of rolling bearings were classified by comparing the minimum Euclidean distances among the detected samples and the normal samples.The proposed method herein was applied to condition assessment of the roll-ing bearings,and the test results show that the fusion index is more sensitive and robust than that of original single feature during the stages of early faults;meanwhile,the fusion index may reflect the states of rolling bearings more accurately.关键词
自组织神经网络/主成分分析/特征融合/最小匹配距离/滚动轴承/故障识别Key words
self-organization mapping(SOM)/principal component analysis(PCA)/feature fu-sion/minimum matching distance/rolling bearing/fault identification分类
航空航天引用本文复制引用
张全德,陈果,林桐,欧阳文理,滕春禹,王洪伟..基于自组织神经网络的滚动轴承状态评估方法[J].中国机械工程,2017,28(5):550-558,9.基金项目
国家自然科学基金资助项目(51675263) （51675263）
