An Adaptive Online Blade Health Monitoring Method: From Raw Data to Parameters Identification

TLDR

Blade tip timing methods are widely used for blade health monitoring, but current approaches suffer from time‑consuming manual resonance region selection and either spectral aliasing or high computational complexity. This study proposes an adaptive online blade health monitoring method that automatically recognizes resonance regions and identifies parameters within them. The method uses cross‑correlation and linear estimation to detect synchronous and asynchronous resonances, then applies an iterative adaptive least‑squares periodogram to identify parameters, with validation on simulated and laboratory data. The approach was validated on engineering data, demonstrating effective online monitoring for both synchronous and asynchronous resonances.

Abstract

Blade tip timing (BTT) methods have been increasingly implemented for blade health monitoring (BHM). However, there are two drawbacks of current signal analysis methods preventing them from applying to online monitoring: first, current online monitoring requires the manual judgment of the resonance region, which is time-consuming. Second, existing BTT resonance signal analysis methods are not suitable for online monitoring. The spectral-analysis-based method presents spectral aliasing, while the computational complexity of the sparse-based method is usually high. In this article, we propose an adaptive online BHM 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 resonances, use the cross correlation to judge the occurrence of synchronous vibration, and use the linear estimation to determine the appearance of asynchronous vibration. For the latter step, an iterative adaptive least-squares periodogram is adopted for its tradeoff between spectral aliasing and computational complexity. The effectiveness of the above steps is first verified using different simulation data separately. Then, the laboratory data are 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.