CN113935460B - 类不平衡数据集下的机械故障智能诊断方法 - Google Patents

类不平衡数据集下的机械故障智能诊断方法 Download PDF

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CN113935460B
CN113935460B CN202111136682.5A CN202111136682A CN113935460B CN 113935460 B CN113935460 B CN 113935460B CN 202111136682 A CN202111136682 A CN 202111136682A CN 113935460 B CN113935460 B CN 113935460B
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CN113935460A (zh
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王俊
戴俊
石娟娟
江星星
姚林泉
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Suzhou University
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    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G06F18/2414—Smoothing the distance, e.g. radial basis function networks [RBFN]
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    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
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    • G—PHYSICS
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    • G06F18/243—Classification techniques relating to the number of classes
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CN202111136682.5A 2021-09-27 2021-09-27 类不平衡数据集下的机械故障智能诊断方法 Active CN113935460B (zh)

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CN202111136682.5A CN113935460B (zh) 2021-09-27 2021-09-27 类不平衡数据集下的机械故障智能诊断方法
PCT/CN2021/123198 WO2023044979A1 (fr) 2021-09-27 2021-10-12 Procédé de diagnostic intelligent de défauts mécaniques d'après un ensemble de données à classes déséquilibrées

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Families Citing this family (17)

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Publication number Priority date Publication date Assignee Title
CN114057053B (zh) * 2022-01-18 2022-04-26 杭州浅水数字技术有限公司 用于特种机械的部件疲劳程度监测方法
CN114611233B (zh) * 2022-03-08 2022-11-11 湖南第一师范学院 一种旋转机械故障不平衡数据生成方法及计算机设备
CN114993677B (zh) * 2022-05-11 2023-05-02 山东大学 不平衡小样本数据的滚动轴承故障诊断方法及系统
CN116204786B (zh) * 2023-01-18 2023-09-15 北京控制工程研究所 生成指定故障趋势数据的方法和装置
CN116401596B (zh) * 2023-06-08 2023-08-22 哈尔滨工业大学（威海） 基于深度指数激励网络的早期故障诊断方法
CN116432091B (zh) * 2023-06-15 2023-09-26 山东能源数智云科技有限公司 基于小样本的设备故障诊断方法、模型的构建方法及装置
CN116993319B (zh) * 2023-07-14 2024-01-26 南京先维信息技术有限公司 一种基于物联网的远程设备健康监测方法及装置
CN116701948B (zh) * 2023-08-03 2024-01-23 东北石油大学三亚海洋油气研究院 管道故障诊断方法及系统、存储介质和管道故障诊断设备
CN116821697B (zh) * 2023-08-30 2024-05-28 聊城莱柯智能机器人有限公司 一种基于小样本学习的机械设备故障诊断方法
CN117056814B (zh) * 2023-10-11 2024-01-05 国网山东省电力公司日照供电公司 一种变压器声纹振动故障诊断方法
CN117056734B (zh) * 2023-10-12 2024-02-06 山东能源数智云科技有限公司 基于数据驱动的设备故障诊断模型的构建方法及装置
CN117076935B (zh) * 2023-10-16 2024-02-06 武汉理工大学 数字孪生辅助的机械故障数据轻量级生成方法及系统
CN117593783B (zh) * 2023-11-20 2024-04-05 广州视景医疗软件有限公司 基于自适应smote的视觉训练方案生成方法及装置
CN117332342B (zh) * 2023-11-29 2024-02-27 北京宝隆泓瑞科技有限公司 一种基于半监督学习的机泵设备运行故障分类方法及装置
CN117725419A (zh) * 2023-12-22 2024-03-19 兰州理工大学 一种小样本类不平衡转子故障诊断方法及系统
CN117909652B (zh) * 2023-12-29 2024-06-25 广东电网有限责任公司江门供电局 一种高压断路器故障诊断数据处理方法
CN117610614B (zh) * 2024-01-11 2024-03-22 四川大学 基于注意力引导的生成对抗网络零样本核电密封检测方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109753998A (zh) * 2018-12-20 2019-05-14 山东科技大学 基于对抗式生成网络的故障检测方法及系统、计算机程序
CN113239991A (zh) * 2021-04-28 2021-08-10 浙江工业大学 基于回归生成对抗网络的火焰图像氧浓度预测方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110428004B (zh) * 2019-07-31 2021-02-05 中南大学 数据失衡下基于深度学习的机械零部件故障诊断方法
CN112396088B (zh) * 2020-10-19 2023-05-12 西安交通大学 一种小样本下隐式激励对抗训练的机械故障智能诊断方法
CN113298230B (zh) * 2021-05-14 2024-04-09 武汉嫦娥医学抗衰机器人股份有限公司 一种基于生成对抗网络的不平衡数据集的预测方法
CN113255078A (zh) * 2021-05-31 2021-08-13 南京信息工程大学 一种样本不均衡条件下的轴承故障检测方法及装置

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109753998A (zh) * 2018-12-20 2019-05-14 山东科技大学 基于对抗式生成网络的故障检测方法及系统、计算机程序
CN113239991A (zh) * 2021-04-28 2021-08-10 浙江工业大学 基于回归生成对抗网络的火焰图像氧浓度预测方法

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