CN114897032B - 一种基于宽度学习的电流互感器故障诊断方法、装置 - Google Patents
一种基于宽度学习的电流互感器故障诊断方法、装置 Download PDFInfo
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CN115326398B (zh) * | 2022-10-17 | 2023-01-24 | 华东交通大学 | 一种基于模糊宽度学习模型的轴承故障诊断方法 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108414226A (zh) * | 2017-12-25 | 2018-08-17 | 哈尔滨理工大学 | 基于特征迁移学习的变工况下滚动轴承故障诊断方法 |
CN110243590A (zh) * | 2019-06-25 | 2019-09-17 | 中国民航大学 | 一种基于主成分分析和宽度学习的转子***故障诊断方法 |
CN110595765A (zh) * | 2019-08-26 | 2019-12-20 | 西安理工大学 | 基于vmd和fa_pnn风电机组齿轮箱故障诊断方法 |
CN111461176A (zh) * | 2020-03-09 | 2020-07-28 | 华南理工大学 | 基于归一化互信息的多模态融合方法、装置、介质及设备 |
CN112285632A (zh) * | 2020-10-20 | 2021-01-29 | 国网湖北省电力有限公司营销服务中心(计量中心) | 一种基于vmd和样本熵的电磁式电流互感器故障诊断方法 |
CN112414713A (zh) * | 2020-11-04 | 2021-02-26 | 吉电(滁州)章广风力发电有限公司 | 一种基于实测信号的滚动轴承故障检测方法 |
CN113486868A (zh) * | 2021-09-07 | 2021-10-08 | 中南大学 | 一种电机故障诊断方法及*** |
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Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
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CN108414226A (zh) * | 2017-12-25 | 2018-08-17 | 哈尔滨理工大学 | 基于特征迁移学习的变工况下滚动轴承故障诊断方法 |
CN110243590A (zh) * | 2019-06-25 | 2019-09-17 | 中国民航大学 | 一种基于主成分分析和宽度学习的转子***故障诊断方法 |
CN110595765A (zh) * | 2019-08-26 | 2019-12-20 | 西安理工大学 | 基于vmd和fa_pnn风电机组齿轮箱故障诊断方法 |
CN111461176A (zh) * | 2020-03-09 | 2020-07-28 | 华南理工大学 | 基于归一化互信息的多模态融合方法、装置、介质及设备 |
CN112285632A (zh) * | 2020-10-20 | 2021-01-29 | 国网湖北省电力有限公司营销服务中心(计量中心) | 一种基于vmd和样本熵的电磁式电流互感器故障诊断方法 |
CN112414713A (zh) * | 2020-11-04 | 2021-02-26 | 吉电(滁州)章广风力发电有限公司 | 一种基于实测信号的滚动轴承故障检测方法 |
CN113486868A (zh) * | 2021-09-07 | 2021-10-08 | 中南大学 | 一种电机故障诊断方法及*** |
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