Article(id=1242116996133884079, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242116994296778923, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1243353600000, receivedDateStr=2009-05-27, revisedDate=-2209017600000, revisedDateStr=1900-01-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1250092800000, onlineDateStr=2009-08-13, pubDate=1250092800000, pubDateStr=2009-08-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1250092800000, onlineIssueDateStr=2009-08-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073369290, creator=sys-migrate, updateTime=1774073369290, updator=sys-migrate, issue=Issue{id=1242116994296778923, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0915', pageStart='3', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='1250092800000', pubDateStr='2009-08-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073368854, creator='sys-migrate', updateTime=1774073368854, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=101, endPage=103, ext={EN=ArticleExt(id=1242116997815796559, articleId=1242116996133884079, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Fault Detection for Bearings Based on Signal Lipschitz Spectrum Entropy, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=It is known that the wavelet-singular point detection-based method is sensitive to noises; to solve this problem, a method of fault detection for bearings based on wavelet transform modulus maximum Lipschitz spectrum entropy is proposed by combining wavelet analysis with entropy theory, including the detection scheme of bearing vibration faults and the threshold selection method based on swarm intelligence. The proposed method is compared with the methods based on wavelet energy spectrum and wavelet packet energy spectrum entropy and the wavelet-singular point detection-based method in the experiments. The results show that the proposed method is particularly well adapted to describe fault characteristics and fault diagnosis, which outperforms the other three methods in terms of detection time and detection rate., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=HwMm9sTwxu6dpD5+a8RGqA==, pdfFileSize=384671, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242116997316677818, articleId=1242116996133884079, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于Lipschitz指数熵的轴承故障检测方法, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=针对利用小波奇异点进行故障检测无法克服噪声影响的不足，提出采用Lipschitz指数熵作为特征进行故障检测。该方法以信号在小波域上分解形成的Lipschitz指数谱向量的熵值作为故障的诊断特征，建立了基于Lipschitz指数熵的故障检测模型，并提出了基于粒子群优化的特征阈值选择方法。将该方法同基于小波能量谱、小波包能量谱熵特征和小波奇异点检测的方法进行比较，实验结果表明采用Lipschitz指数熵作为特征都能有效克服噪声影响，在检测时间及检测率上较另外3种方法有显著提高。, authors=徐 晶，张秋杰，单 净，姜 萍, authorsList=徐 晶;张秋杰;单 净;姜 萍, authorCompany=黑龙江科技学院数学力学系, correspAuthors=null, authorNote=null, correspAuthorsNote=徐晶, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=bEx8jfg580Rd0LPoVnXU0A==, pdfFileSize=384671, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, keywords=[Keyword(id=1242116996922413236, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242116996133884079, language=CN, orderNo=1, 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