Article(id=1202984228507054505, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1202984226762219866, articleNumber=1009-5438(2022)03-0068-05, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1648483200000, receivedDateStr=2022-03-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1764743390442, onlineDateStr=2025-12-03, pubDate=1656086400000, pubDateStr=2022-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764743390442, onlineIssueDateStr=2025-12-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764743390442, creator=13701087609, updateTime=1764743390442, updator=13701087609, issue=Issue{id=1202984226762219866, tenantId=1146029695717560320, journalId=1185652524569653253, year='2022', volume='48', issue='3', pageStart='1', pageEnd='98', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764743390026, creator=13701087609, updateTime=1764744768237, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1202990007461049256, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1202984226762219866, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1202990007461049257, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1202984226762219866, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=68, endPage=72, ext={EN=ArticleExt(id=1202984228779684270, articleId=1202984228507054505, tenantId=1146029695717560320, journalId=1185652524569653253, language=EN, title=Establishment of Self Diagnosis Model for Rolling Bearing Faults Based on Wavelet Packet Decomposition, columnId=1187100786392711543, journalTitle=Science & Technology of Baotou Steel, columnName=Equipment and Automation, runingTitle=null, highlight=null, articleAbstract=

In the paper, the scheme of establishing a self diagnosis model for high speed rolling bearing is proposed considering the complexity of rolling bearing fault diagnosis and combining with the difficulties of rolling bearing fault diagnosis for equipment on site. The original vibration signal of rolling bearing for high speed gear box is decomposed using the wavelet packet, then frequency domain signals in different frequency bands in decomposed original vibration signal are reconstructed and the characteristics of shock pulse energy for rolling bearing fault of reconstructed frequency domain signals are extracted respectively. The extraction of characteristics for rolling bearing fault of high speed gear box is realized and they are used for establishing the self diagnosis model for high speed rolling bearing through comparing the characteristics of impact energy for rolling bearing extracted from each frequency band after reconstruction as well as extracting frequency band signals sensitive to energy changes aiming at different types of rolling bearings.

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考虑滚动轴承故障诊断的复杂性,结合现场设备滚动轴承故障诊断的痛点,文章提出了一种高速滚动轴承自诊断模型建立方案。利用小波包分解高速齿轮箱滚动轴承原始振动信号,再将分解后的原始振动信号中不同频带内的频域信号进行重构,分别对重构的频域信号提取滚动轴承故障冲击脉冲能量特征,通过对比重构后各频段提取的滚动轴承冲击能量特征,针对不同类型的滚动轴承提取能量变化敏感的频带信号,实现对高速齿轮箱滚动轴承故障特征的提取并用于建立高速滚动轴承故障自诊断模型。

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宁中赫(1987-),男,内蒙古包头市人,硕士,工程师,现从事设备运行管理工作。

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