Identification of Asynchronous Blade Vibration Parameters by Linear Regression of Blade Tip Timing Data

Fracture of blades is usually catastrophic and creates serious damages in the turbomachines. Blades are subjected to high centrifugal force, oscillating stresses, and high temperature which makes their life limited. Therefore, blades should be checked and replaced at specified intervals or utilize a health monitoring method for them. Crack detection by nondestructive tests can only be performed during machine overhaul which is not suitable for monitoring purposes. Blade tip timing (BTT) method as a noncontact monitoring technique is spreading for health monitoring of the turbine blades. One of the main challenges of BTT method is identification of vibration parameters from one per revolution samples which is quite below Nyquist sampling rate. In this study, a new method for derivation of blade asynchronous vibration parameters from BTT data is proposed. The proposed method requires only two BTT sensors and applies least mean square algorithm to identify frequency and amplitude of blade vibration. These parameters can be further used as blade health indicators to predict defect growth in the blades. Robustness of the proposed method against measurement noise which is an important factor has been examined by numerical simulation. An experimental test was conducted on a bladed disk to show efficiency of the proposed method.