Parameter identification based on iterative signal space in blade tip timing

Blade tip timing (BTT), as an effective non-contact measurement technology, is widely used for health monitoring of rotating blades. Due to the installed number of probes is limited by the engine structure, the requirement of BTT sampling frequency is difficult to meet the Nyquist sampling theorem, which leads the BTT signal is severely under-sampled. Therefore, it is difficult to extract vibration parameters, such as frequency, amplitude. Many algorithms was proposed to solve the undersampling problem. In this paper, a new BTT signal processing algorithm is proposed based iterative signal space, which expands individual data into subspace level data geometrically. Compared to some previous methods, the iterative signal space algorithm can identify the blade vibration parameters more accurately, which is verified in both numerical simulation and experiment. Furthermore, different degrees of noise is used to show the feasibility and robustness. The results are very significant for online blade fault diagnosis and health monitoring of turbines.