Vibration-based health monitoring of aero-engine bearings

该项目将开发新的基于振动的技术，以极大地提高航空发动机轴承故障的检测和诊断能力。为了实现这一目标，将在提取微弱轴承信号的源分离算法以及在广泛的运行条件下提取用于诊断轴承故障严重性和润滑状况的特征的信号处理技术方面取得进展。轴承退化模型将估计剩余的使用寿命。由于滚动轴承是大多数机器中最关键的部件之一，这项研究的结果还将在采矿、交通、能源生产和制造等其他领域提供巨大的好处。

This project will develop new vibration-based techniques to greatly improve the detection and diagnosis of faults in aero engine bearings from in-flight measurements. To achieve this goal, advances will be made on source separation algorithms to extract the weak bearing signals, and signal processing techniques to extract features for diagnosing bearing fault severity and lubrication conditions, under a wide range of operating conditions. A bearing degradation model will estimate the remaining useful life. Since rolling element bearings are among the most critical components in most machines, the results of this research will also provide massive benefits in other sectors such as mining, transportation, energy production and manufacturing.