LFPKMPPM - Lubrication Failure Prediction of Key Mechanical Parts for Predictive Maintenance

合理的预知维修策略对降低大型机械设备的维修成本具有重要意义，关键在于对关键机械零部件进行早期故障行为监测和预测。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。为了降低它和控制能源价格，预测性维护策略变得比以往任何时候都更加重要。作为风电机组的关键支撑部件，重载轴承承载的载荷最大，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，本项目致力于基础性研究，包括如何开发一种用于在线监测润滑健康相关变量的超声波测量方法，以及如何动态地表征和预测润滑故障。具体地说，该项目利用超声波反射现象开发了：i)通过声学有限元模拟在线同时测量最小油膜厚度和表面粗糙度的方法和回波特征的映射模型；ii)基于模糊模式识别的实时润滑状态表征；以及iii)具有自更新的Stribeck曲线的润滑状态数据模型融合预测方法。总体而言，该项目的重点是i)同时测量油膜厚度和表面粗糙度；ii)识别重负荷滚子轴承的润滑状态和预测润滑故障；iii)消除阻碍大型发电设备可靠运行和维护的困难。

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.