High-resolution sampling aliasing frequency distribution

Rotating blades are core but fragile working parts in turbomachinery, and damage to the rotating blades severely endangers operational safety; thus, it is critical to monitor the blade condition to find the incipient failure. Blade tip timing (BTT) is a potential measurement for rotating blades owing to its non-contact nature, high efficiency, and long service life. Identifying vibration parameters for condition monitoring is a core issue. Most existing BTT methods rely on multi-probe layouts to overcome undersampling to extract parameters. However, restricted by weight, safety, installation, and maintenance costs, it is desired to extract vibration parameters on the premise of the minimal layout. Motivated by this demand, we propose a single probe -based parameter identification method, where sampling aliasing frequency (SAFE) distribution plays a significant role. To improve the accuracy of parameter identification, a synchrosqueezing adaptive window length short-time Fourier transform (SS-AWSTFT) is proposed to construct a high-resolution SAFE distribution. Moreover, a new fitting model considering rotational effect is developed to extract engine order (EO), static frequency, and dynamic frequency coefficient from the SFAE distribution. Owing to considering the rotational effect, the new fitting model performs better in parameter estimation. Finally, the effectiveness and robustness of the proposed method were demonstrated by simulations and experiments.

Automated summary

This research proposes a single probe-based method for identifying vibration parameters of rotating blades, and constructs a high-resolution sampling aliasing frequency distribution using the synchrosqueezing adaptive window length short-time Fourier transform to improve the accuracy of parameter estimation. Moreover, a new fitting model that considers the rotational effect is developed to extract vibration parameters from the distribution.