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Structural components of gas turbines, particularly the blades, sustain a variety of damages during the operation process. The most frequent cause of these damages are the overheating and thermal fatigue of the material. A primary technique to assess condition of the blades is the metallographic examination. In spite of the fact that metallographic analysis delivers much more information on the structure of examined blade material, it is a type of destructive test resulting in the destruction of the blade which makes further utilization of the item impossible. The paper has been intended to discuss non-destructive testing methods and to present capabilities of applying them to diagnose objectively changes in the microstructure of a turbine blade with computer software engaged to assist with the analyses. The following techniques are discussed: a visual method, based on the processing of images of the material surface in visible light, active thermography, based on the detection of infrared radiation, and the X-ray computed tomography. All these are new non-destructive methods of assessing technical condition of structural components of machines. They have been intensively developed at research centers worldwide, and in Poland. The computer-aided visual method of analyzing images enables diagnosis of the condition of turbine blades, without the necessity of dismantling of the turbine. On the other hand, the active thermography and the X-ray computed tomography, although more sensitive and more reliable, can both be used with the blades dismounted from the turbine. If applied in a complex way, the non-destructive methods presented in this paper, are expected to increase significantly probability of detecting changes in the blade’s condition, which in turn would be advantageous to reliability and safety of gas turbine service

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Acta Mechanica et Automatica

The Journal of Bialystok Technical University

Journal Information

CiteScore 2017: 1.07

SCImago Journal Rank (SJR) 2017: 0.361
Source Normalized Impact per Paper (SNIP) 2017: 0.917

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