LFPKMPPM - Lubrication Failure Prediction of Key Mechanical Parts for Predictive Maintenance
LFPKMPPM - 关键机械部件润滑故障预测,用于预测性维护
基本信息
- 批准号:EP/Y028198/1
- 负责人:
- 金额:$ 23.84万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Fellowship
- 财政年份:2023
- 资助国家:英国
- 起止时间:2023 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
An appropriate predictive maintenance strategy is significant for reducing the maintenance cost of large mechanical equipment, and the key lies in early failure behaviour monitoring and prediction of key mechnical parts. The growing demand for wind power in Europe incurs an exponential rise in maintenance costs. To reduce it and control the energy price, predictive maintenance strategies are becoming more important than ever before. As the key support component in a wind turbine, the heavy-loaded bearing supports the most load and is the most vulnerable part (this accounts for 76% of mechanical failures). Evidently, the earlier warning of most unrecoverable failures remains a blind spot due to unreliable technologies for monitoring and predicting early lubrication failure. Focusing on this, the project aims at fundamental research including i) how to develop an ultrasonic measurement method for online monitoring of lubrication-health related variables and ii) how to dynamically characterize and predict lubrication failure. Specifically, this project employs the ultrasonic reflection phenomenon to develop: i) an online simultaneous measurement method of the minimum oil film thickness and surface roughness by acoustic finite element simulation and a mapping model of echo features; ii)real-time lubrication state characterization enabled by fuzzy pattern recognition; and iii) a data-model fusion prediction method of lubrication states with a self-updated Stribeck curve. Overall, this project highlights i) the simultaneous measurement of oil film thickness and surface roughness; ii) the identification of lubrication state and the prediction of lubrication failure in heavy-loaded roller bearings, and iii) the elimination of difficulties that obstruct reliable operation and maintenance of large power generation equipment.
合理的预测维修策略对降低大型机械设备的维修成本具有重要意义,而关键部件的早期故障行为监测与预测是关键。欧洲对风力发电的需求不断增长,导致维护成本呈指数级上升。为了降低能耗并控制能源价格,预测性维护策略变得比以往任何时候都更加重要。重载轴承作为风力涡轮机中的关键支撑部件,承受的载荷最大,也是最脆弱的部件(占机械故障的76%)。显然,由于监测和预测早期润滑故障的技术不可靠,大多数不可恢复故障的早期警告仍然是一个盲点。围绕这一点,该项目旨在进行基础研究,包括i)如何开发用于在线监测润滑健康相关变量的超声波测量方法,以及ii)如何动态表征和预测润滑故障。具体而言,该项目采用超声波反射现象开发:i)通过声学有限元模拟和回波特征映射模型在线同时测量最小油膜厚度和表面粗糙度的方法; ii)通过模糊模式识别实现实时润滑状态表征;以及iii)具有自更新Stribeck曲线的润滑状态数据模型融合预测方法。总的来说,该项目的重点是i)油膜厚度和表面粗糙度的同时测量; ii)重载滚子轴承润滑状态的识别和润滑故障的预测; iii)消除阻碍大型发电设备可靠运行和维护的困难。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Pan Dou其他文献
A randomized double-blind placebo-controlled study of Pu’er tea (普洱茶) extract on the regulation of metabolic syndrome
- DOI:
10.1007/s11655-011-0781-4 - 发表时间:
2011-07-03 - 期刊:
- 影响因子:2.500
- 作者:
Song-ling Chu;Hong Fu;Jin-xia Yang;Geng-xin Liu;Pan Dou;Liang Zhang;Peng-fei Tu;Xue-mei Wang - 通讯作者:
Xue-mei Wang
Simultaneous measurement of thickness and sound velocity of porous coatings based on the ultrasonic complex reflection coefficient
- DOI:
10.1016/j.ndteint.2022.102683 - 发表时间:
2022 - 期刊:
- 影响因子:
- 作者:
Pan Dou;Laisheng Zou;Tonghai Wu;Min Yu;Tom Reddyhoff;Zhongxiao Peng - 通讯作者:
Zhongxiao Peng
Ultrasound enabled simultaneous measurement of coating wear depth and lubricant film thickness in a sliding bearing
超声技术能够同时测量滑动轴承中的涂层磨损深度和润滑油膜厚度
- DOI:
10.1016/j.measurement.2024.115602 - 发表时间:
2025-01-30 - 期刊:
- 影响因子:5.600
- 作者:
Pan Dou;Peiping Yang;Peng Zheng;Yaping Jia;Tonghai Wu;Shuo Wang;Min Yu - 通讯作者:
Min Yu
In-situ adaptive calibration of incident signal of ultrasonic lubricant film thickness measurements
- DOI:
10.1016/j.ymssp.2024.112236 - 发表时间:
2025-02-01 - 期刊:
- 影响因子:
- 作者:
Quanzhong Wu;Pan Dou;Shiyuan Chang;Tonghai Wu;Min Yu;Yaguo Lei - 通讯作者:
Yaguo Lei
Ultrasonic reflection-based transient temperature gradient inversion for compensating oil film thickness measurement
基于超声反射的瞬态温度梯度反演用于油膜厚度测量补偿
- DOI:
10.1016/j.ymssp.2025.112827 - 发表时间:
2025-07-01 - 期刊:
- 影响因子:8.900
- 作者:
Yaping Jia;Shiyuan Chang;Yonggang Xia;Tonghai Wu;Peiping Yang;Pan Dou;Min Yu - 通讯作者:
Min Yu
Pan Dou的其他文献
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