One of the major potential faults in a turbo fan engine is the crack initiation and propagation in bladed discs under cyclic loads that could result in the breakdown of the engines if not detected at an early stage. Reliable fault detection techniques are therefore in demand to reduce maintenance cost and prevent catastrophic failures. Although a number of approaches have been reported in the literature, it remains very challenging to develop a reliable technique to accurately estimate the health condition of a rotating bladed disc. Correspondingly, this paper presents a novel technique for bladed disc crack detection through two sequential signal processing stages: (1) signal preprocessing that aims to eliminate the noises in the blade passage signals; (2) signal postprocessing that intends to identify the crack location. In the first stage, physics-based modeling and interpretation are established to help characterize the noises. The crack initiation can be determined based on the calculated health monitoring index derived from the sinusoidal effects. In the second stage, the crack is located through advanced detrended fluctuation analysis of the preprocessed data. The proposed technique is validated using a set of spin rig test data (i.e. tip clearance and time of arrival) that was acquired during a test conducted on a bladed military engine fan disc. The test results have demonstrated that the developed technique is an effective approach for identifying and locating the incipient crack that occurs at the root of a bladed disc.


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    Title :

    Bladed disc crack diagnostics using blade passage signals


    Contributors:
    Hanachi, Houman (author) / Liu, Jie (author) / Banerjee, Avisekh (author) / Koul, Ashok (author) / Liang, Ming (author) / Alavi, Elham (author)

    Published in:

    Measurement Science and Technology ; 23 , 12 ; 125003/1-125003/8


    Publication date :

    2012


    Size :

    8 Seiten, 20 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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