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1.四川轻化工大学 自动化与信息工程学院, 四川 宜宾 643002
2.人工智能四川省重点实验室, 四川 宜宾 643002
李兆飞(1982— ),男,四川雅安人,博士,副教授,硕士生导师;研究方向为设备状态监测及故障诊断,人机交互及智能信息处理;lizhaofei825@163.com。
纸质出版日期:2023-03-15,
收稿日期:2022-03-04,
修回日期:2022-05-06,
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李兆飞.数据驱动的轴承早期故障诊断技术综述[J].机械传动,2023,47(03):165-176.
Li Zhaofei.A Survey of Incipient Fault Diagnosis of Bearings Based on Data-drive[J].Journal of Mechanical Transmission,2023,47(03):165-176.
李兆飞.数据驱动的轴承早期故障诊断技术综述[J].机械传动,2023,47(03):165-176. DOI: 10.16578/j.issn.1004.2539.2023.03.022.
Li Zhaofei.A Survey of Incipient Fault Diagnosis of Bearings Based on Data-drive[J].Journal of Mechanical Transmission,2023,47(03):165-176. DOI: 10.16578/j.issn.1004.2539.2023.03.022.
轴承早期故障的实时诊断,是实际工程应用需求和基础科学问题研究的交汇点,是轴承故障诊断的发展方向之一。首先,阐述了轴承故障及演变过程;其次,根据轴承早期故障实时诊断的需求,总结了轴承早期故障诊断难点问题;之后,重点论述了轴承早期故障诊断3个关键环节所采用的各种技术:微小监测信号增强技术、监测数据的融合表示技术以及早期故障智能诊断技术;最后,总结展望了轴承早期故障诊断技术的发展趋势。
On-line real time diagnosis of bearing incipient faults is the intersection of practical engineering application requirements and basic scientific research. It is one of the development directions of bearing fault diagnosis at home and abroad. Firstly
this study analyzes the bearing fault and its evolution process; secondly
according to the needs of bearing incipient fault diagnosis in time
the difficult problems of bearing incipient fault diagnosis are summarized; then
it focuses on the various technologies used in the three crucial links of bearing early fault diagnosis: the weak monitoring signal enhancement technology
the fusion representation technology of monitoring parameters and the early fault intelligent diagnosis technology; finally
the development trend of bearing incipient fault diagnosis technology is summarized and prospected.
轴承微小故障早期故障诊断盲源分离深度迁移学习
BearingMinor faultIncipient fault diagnosisBlind source separationDeep transfer learning
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