Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals
Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains ver...
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ndltd-LACETR-oai-collectionscanada.gc.ca-OOU.#10393-241702014-06-14T03:49:58ZBladed Disk Crack Detection Through Advanced Analysis of Blade Passage SignalsAlavifoumani, Elhamosadatcrack detectionturbo fan enginebladed disksignal processingwavelet analysisdetrended fluctuation analysisfeature extractionCrack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions.2013-05-14T20:32:34Z2013-05-14T20:32:34Z20132013-05-14Thèse / Thesishttp://hdl.handle.net/10393/24170en |
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en |
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crack detection turbo fan engine bladed disk signal processing wavelet analysis detrended fluctuation analysis feature extraction |
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crack detection turbo fan engine bladed disk signal processing wavelet analysis detrended fluctuation analysis feature extraction Alavifoumani, Elhamosadat Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
description |
Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions. |
author |
Alavifoumani, Elhamosadat |
author_facet |
Alavifoumani, Elhamosadat |
author_sort |
Alavifoumani, Elhamosadat |
title |
Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
title_short |
Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
title_full |
Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
title_fullStr |
Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
title_full_unstemmed |
Bladed Disk Crack Detection Through Advanced Analysis of Blade Passage Signals |
title_sort |
bladed disk crack detection through advanced analysis of blade passage signals |
publishDate |
2013 |
url |
http://hdl.handle.net/10393/24170 |
work_keys_str_mv |
AT alavifoumanielhamosadat bladeddiskcrackdetectionthroughadvancedanalysisofbladepassagesignals |
_version_ |
1716669585289117696 |