Abstract

Turbine blades are critical machine components in power plants and aerospace turbo engines. Failure of these blades in operation leads to catastrophic damages as well as high cost of maintenance and repair. Blades are often assembled in packets with lacing wire or shroud ring interconnections. Natural frequencies of the bladed packets are designed in a specific range to avoid possible resonant stresses. However, frequent damages during operation alter the stiffness of the blade-packet assembly and change the eigen-spectrum. A numerical study is presented in this work, where it is demonstrated that characteristic changes in eigen-spectrum can identify both severity and location of such damages. The work employs matrix perturbation theory on the eigen-value problem, formulated from the lumped-parameter modeling of the blade packet. Damage is considered as a perturbation in the stiffness matrix with damage severity acting as the perturbation parameter. First, a graphical pattern recognition method, and then, a damage proximity index evaluation method is suggested for damage identification. Further, an estimation algorithm for damage severity is presented with numerically simulated computations, which demonstrates that the methods can exactly identify the damage location and, with very little error, can estimate the damage severity.

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