Research on Frequency Identification Method for Rotating Blades Based on the Sampling-Aliasing Frequency Map

The rotating blades are key components of significant equipment such as gas turbines and aero engines, and their health status will directly affect the safety and reliability of these equipment. The vibration signal contains a wealth of blade fault information, from which the health status of the working blades can be monitored. Blade tip timing (BTT) is an effective method for monitoring blade vibration, but the signals collected by this method suffer from undersampling. A sampling-aliasing frequency (SAFE) map can be obtained by Adaptive window length short-time Fourier transform (AWSTFT) on under-sampled BTT signals, from which we can identify the synchronous vibration frequency and engine order (EO) of rotating blades. The paper studies the natural frequency identification method of rotating blades based on the SAFE map. Firstly, a BTT measurement model is established, and the effects of parameters such as frequency resolution and window overlap coefficient on the quality of SAFE maps are studied. Subsequently, a BTT test rig is built, and the method is used to identify the natural frequencies of 8 rotating blades on the impeller. The identification results are compared with the results of blade modal testing, verifying the method's effectiveness.