组合机床与自动化加工技术
組閤機床與自動化加工技術
조합궤상여자동화가공기술
Modular Machine Tool & Automatic Manufacturing Technique
2015年
11期
88-90
,共3页
轴承故障诊断%时域指标%主成分分析%支持向量机
軸承故障診斷%時域指標%主成分分析%支持嚮量機
축승고장진단%시역지표%주성분분석%지지향량궤
bearing fault diagnosis%time domain%PCA%SVM
针对滚动轴承振动信号的时域指标之间存在很强的关联性,冗余信息较多,采用主成分分析结合支持向量机实现了滚动轴承故障的准确诊断. 首先在故障模拟试验台测量振动信号,然后提取振动信号的12个时域特征,对12个基本时域特征进行主成分分析,提取累计贡献率≥95的特征值信息作为主成分. 最后将提取的精简特征作为支持向量机的输入,实现对不同轴承故障的分类识别.实验结果证明针对四种轴承状态,识别率达到90%,提出的结合PCA-SVM是一种有效的滚动轴承故障诊断方法.
針對滾動軸承振動信號的時域指標之間存在很彊的關聯性,冗餘信息較多,採用主成分分析結閤支持嚮量機實現瞭滾動軸承故障的準確診斷. 首先在故障模擬試驗檯測量振動信號,然後提取振動信號的12箇時域特徵,對12箇基本時域特徵進行主成分分析,提取纍計貢獻率≥95的特徵值信息作為主成分. 最後將提取的精簡特徵作為支持嚮量機的輸入,實現對不同軸承故障的分類識彆.實驗結果證明針對四種軸承狀態,識彆率達到90%,提齣的結閤PCA-SVM是一種有效的滾動軸承故障診斷方法.
침대곤동축승진동신호적시역지표지간존재흔강적관련성,용여신식교다,채용주성분분석결합지지향량궤실현료곤동축승고장적준학진단. 수선재고장모의시험태측량진동신호,연후제취진동신호적12개시역특정,대12개기본시역특정진행주성분분석,제취루계공헌솔≥95적특정치신식작위주성분. 최후장제취적정간특정작위지지향량궤적수입,실현대불동축승고장적분류식별.실험결과증명침대사충축승상태,식별솔체도90%,제출적결합PCA-SVM시일충유효적곤동축승고장진단방법.
The time domainindicatorsof vibration signalof rolling bearing have a strong correlation and more redundant information. So a bearing fault diagnosis method based on Principal Component Analysis ( PCA) and Support Vector Machine ( SVM) is proposed. Firstly, vibration signalsare measuredby acceleration sen-sor on the fault simulation test bench, and then the principal component are extracted by the method of PCA based on the basic time-domain features of vibration signals. Finally, bearing fault diagnosis is achieved by usingSVM. Experiment results show that the recognition rate is beyond 90% for four bearing conditions, the method based on PCA and SVM is very effective for rolling bearing fault diagnosis.
