Super-Resolution Power Spectrum Estimation for Blade Tip Timing Measurement

Rotating blades are functional and fragile components with high failure risk in turbomachinery; thus, it is necessary to monitor rotating blades to detect incipient failure and safeguard the efficiency and safety of equipment. Blade tip timing (BTT) is a vibration measurement technique for rotating blades and is regarded as a promising approach for blade health monitoring owing to its non-contact nature, high measurement efficiency, and long service life. However, due to the mismatch between the high vibration frequency of the blade and the low sampling rate of BTT measurement, BTT signal is severely undersampled. To overcome the undersampling issue and identify vibration parameters for fault diagnosis, we proposed a super-resolution power spectrum estimation for non-uniform and undersampled BTT signals. The proposed method involves two aspects: covariance recovery and power spectrum estimation. First, we build a covariance representation model and recover a complete covariance matrix. Then, the complete covariance matrix is used to estimate super-resolution power spectrum by generalized subspace method. Furthermore, a series of specific layouts are provided that guarantee the performance of the proposed method. Finally, the effectiveness of the proposed method is validated by both numerical simulations and BTT experiments.