以航空发动机与船舰燃气轮机为代表的高端透平机械在高效率，大机动飞行等工况要求下，内部结构趋于紧凑化和复杂化，使得内部转静子发生碰摩的概率也随之增加，由于其内部少测点、难监测，往往难以及时准确地发现碰摩故障。本文提出了一种叶尖间隙时频特征融合的转子碰摩故障诊断方法，首先通过叶尖振动-间隙复合传感器获取叶尖间隙（Blade Tip Clearance, BTC）序列，然后通过机器学习算法评估BTC序列的异常性；若有异常警报，则对异常信号进行傅里叶分解并汇总各分量的时频能量谱；最后通过时频能量谱中的碰摩带判断故障的发生。实验结果表明，在对转子施加短时、持续碰摩两种工况后，机器学习模型能够实时诊断，且报警后所提方法能完整分辨出两种碰摩的发生，以及持续碰摩缓慢施加、快速结束的过程。通过与转子振动信号进行对比，表明该方法可通过机匣测点获取叶尖间隙并感知转子碰摩故障，实现了“一传多感”，为透平机械的状态监测和健康管理提供了新的方法。
Abstract
High-end turbomachinery represented by aero-engine and shipboard gas turbine in high efficiency, large maneuvering flight and other working conditions, the internal structure tends to be compact and complex, so that the probability of internal rotor stator rubbing increases, due to the internal less measurement points, difficult to monitor, often difficult to accurately find rubbing faults in a timely manner. In this paper, we propose a rotor rubbing fault diagnosis method by fusing the time-frequency characteristics of the blade tip clearance. Firstly, we obtain the Blade Tip Clearance (BTC) sequence through the blade tip vibration-clearance composite sensor, and then we evaluate the abnormality of the BTC sequence through the machine learning algorithm; if there is an abnormal alarm, we carry out Fourier decomposition of the abnormal signals and summarize the time-frequency energy spectrum of the components. Finally, the occurrence of faults is determined by the rubbing bands in the time-frequency energy spectrum. The experimental results show that the machine learning model is able to diagnose the rotor in real time after applying short-time and continuous rubbing conditions, and the proposed method can completely separate the occurrence of the two types of rubbing and the process of continuous rubbing that is applied slowly and ends quickly after the alarm. Comparison with the rotor vibration signal shows that the method can obtain the BTC and sense rotor rubbing faults through the magazine measuring point, realizing "one sensor and many messages", which provides a new method for the condition monitoring and health management of turbomachinery.
关键词
叶尖感知 /
碰摩故障 /
机器学习 /
傅里叶分解 /
叶尖间隙（BTC）
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Key words
blade tip sensing /
rubbing faults /
machine learning /
Fourier decomposition /
blade tip clearance (BTC)
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