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结合DBN和CHMM的滚动轴承性能退化评估

潘玉娜 魏婷婷 程道来

潘玉娜,魏婷婷,程道来. 结合DBN和CHMM的滚动轴承性能退化评估[J]. 机械科学与技术,2023,42(3):462-467 doi: 10.13433/j.cnki.1003-8728.20200600
引用本文: 潘玉娜,魏婷婷,程道来. 结合DBN和CHMM的滚动轴承性能退化评估[J]. 机械科学与技术,2023,42(3):462-467 doi: 10.13433/j.cnki.1003-8728.20200600
PAN Yu'na, WEI Tingting, CHENG Daolai. Assessment of Rolling Bearing performance Degradation Using DBN and CHMM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 462-467. doi: 10.13433/j.cnki.1003-8728.20200600
Citation: PAN Yu'na, WEI Tingting, CHENG Daolai. Assessment of Rolling Bearing performance Degradation Using DBN and CHMM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 462-467. doi: 10.13433/j.cnki.1003-8728.20200600

结合DBN和CHMM的滚动轴承性能退化评估

doi: 10.13433/j.cnki.1003-8728.20200600
基金项目: 国家重点研发计划(2020YFB2007700)、上海市科委地方院校能力建设项目(17090503500)及上海应用技术大学跨学科研究生团队项目(GN203006020-B20)
详细信息
    作者简介:

    潘玉娜(1981−),讲师,博士,研究方向为振动信号分析、故障诊断,panyuna@sit.cn.com

    通讯作者:

    程道来,教授,博士生导师,daolaicheng@163.com

  • 中图分类号: TH133.33;TH17

Assessment of Rolling Bearing performance Degradation Using DBN and CHMM

  • 摘要: 针对现有退化评估方法应用情境单一,特征指标筛选依赖人工经验,提出了一种基于深度置信网络(Deep belief network, DBN)和连续隐马尔科夫(Continuous hidden markov model, CHMM)相结合的滚动轴承性能退化评估方法。将滚动轴承正常状态下的振动信号处理为归一化幅值谱,以此作为DBN特征自动提取模型的输入,并使用CHMM做评估模型,其中CHMM的训练样本即通过DBN提取的正常状态下的特征向量。通过不同情境下的滚动轴承全寿命周期实验数据验证了所提模型的有效性。与近期有关文献所提方法进行比较,该方法避免了人工选择特征指标,且对早期微弱故障检测具有一定的敏感性。
  • 图  1  DBN结构简图

    图  2  DBN和CHMM相结合的评估模型建立流程

    图  3  B1全寿命周期PV指标

    图  4  B2全寿命周期PV指标

    图  5  辛辛那提数据全寿命周期D指标

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出版历程
  • 收稿日期:  2021-03-30
  • 刊出日期:  2023-03-25

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