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Condition Monitoring of Gas Turbines using Acoustic Emissions

Title:

Condition Monitoring of Gas Turbines using Acoustic Emissions

Shahkar, Shahram (2018) Condition Monitoring of Gas Turbines using Acoustic Emissions. Masters thesis, Concordia University.

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Abstract

Acoustic emission (AE) technology has recently found its way in condition monitoring of rotary equipment due to its advantage of earlier detection of defects and anoma- lies in comparison to vibration analysis. However, there has been very little industrial application of AE signals for condition monitoring of safety-critical equipment if any, partly due to the diffculty in processing, interpreting and classifying the acquired data in a highly reliable fashion. The motivation in this thesis was to develop a methodol- ogy for inferring health related information in a gas turbine without intruding the engine.
Our work has targeted a broad class of rotary equipment known as cyclostationary processes, therefore, instead of analyzing particular AE samples of gas turbines we have tried to build a mathematical framework that would suit any arbitrary machine comply- ing certain conditions. The result of our work mainly encompasses a feature extraction technique that eliminates the random e↵ects associated with a gas turbine AE signal, and a hypothetical testing method for classification of AE signals with any desirable level of certainty, subject to a set of assumptions and conditions.
We have validated our methodologies and derivations using actual real-life gas turbine AE signals, and compared our solutions with some of the techniques published in the literature.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Shahkar, Shahram
Institution:Concordia University
Degree Name:M. Sc.
Program:Electrical and Computer Engineering
Date:12 June 2018
Thesis Supervisor(s):Khorasani, Khashayar
Keywords:Acoustic Emissions, Condition Monitoring,
ID Code:983945
Deposited By: Shahram Shahkar
Deposited On:16 Nov 2018 16:18
Last Modified:16 Nov 2018 16:18
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