An Adaptive On-line Blade Health Monitoring Method: from Raw Data to Parameters Identification

Blade Tip Timing (BTT) methods have been increasingly implemented for blade health monitoring. However, there are two drawbacks of current signal analysis methods preventing them from applying to on-line monitoring: firstly, current on-line monitoring requires the manual judgment of the resonance region which is time-consuming. Secondly, existing BTT resonance signal analysis methods are not suitable for on-line monitoring. Spectral-analysis based method present spectral aliasing while the computational complexity of the sparse based method is usually high. In this paper, we propose an adaptive on-line blade health monitoring method that includes two steps: automatic resonance region recognition and parameters identification in resonance area. For the former step, we demonstrate different methods for synchronous and asynchronous resonance, we use the cross-correlation to judge the occurrence of synchronous vibration and use linear estimation to determine the appearance of asynchronous vibration. For the latter step, an iterative adaptive least-squares periodogram is adopted for its trade-off between spectral aliasing and computational complexity. The effectiveness of the above steps is firstly verified using different simulation data separately. Then, the laboratory data is used to test the effectiveness of the whole method. Finally, the online monitoring function of the proposed method is verified by the engineering data with both synchronous and asynchronous resonances.