Abstract
Online monitoring of high-speed rotating blades has always been a hot topic. Of the various methods, the blade tip timing (BTT) technique, based on eddy current sensors, is considered to be the most promising. However, BTT signals are easily influenced by various factors, which means that the accurate extraction of BTT signals remains a challenge. To try to solve this problem, the causes of measurement error were analyzed. The three main reasons for the error were established: the variation in blade tip clearance, the interference of background noise, and the asymmetry of the blade tip shape. Further, pertinent improvement methods were proposed, and a compensation method was proposed for the errors caused by the variation of tip clearance. A new denoising and shaping algorithm based on ensemble empirical mode decomposition (EEMD) was introduced for the errors caused by background noise. Additionally, an optimal installation position of the sensor was also proposed for the errors caused by the asymmetry of the blade tip shape. Finally, simulations and experiments were used to demonstrate the improved methodology. The results show that the measurement error on vibration amplitude and vibration frequency using the proposed method is less than 2.89% and 0.17%, respectively, which is much lower than the traditional method (24.44% and 0.39%, respectively).
Highlights
High-speed rotating blades are the core mechanical components in turbomachinery
This paper focuses on the fact that it is difficult to extract the blade tip timing (BTT) signal based on eddy current sensors accurately
The causes of measurement error in the BTT system using the eddy current sensor include the variation of blade tip clearance, the interference of background noise, and the asymmetry of the blade tip shape
Summary
High-speed rotating blades are the core mechanical components in turbomachinery. Monitoring its vibration state has always been a research hotspot [1,2,3]. BTT technology records the arrival times of the blade tip by sensor probes mounted on the equipment casing; the blade vibration parameters can be obtained by using these time series [6] It is the key of BTT technology to measure these time series accurately. No matter which kind of sensors are used, the original pulse signals are always disturbed by background noise or other factors, which means that BTT signals extracting accurately is a formidable task. This is why the BTT technique has successfully demonstrated its potential, but few high-quality data are still available [10].
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