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Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges
Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges
In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by “compressing” them using the kernel principal component analysis so that more meaningful, lower-dimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM) algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and air-to-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the OCSVM, the kernel width γ and optimization penalty parameter ν. This selection was made by searching along a fixed grid space of values and choosing the combination that provided the highest cross-validation accuracy. Nevertheless, there exist challenges that are discussed along with suggestions for future work that can be used to enhance similar novelty detection schemes.
Matthaiou, Ioannis
7855a890-8929-4c90-a08c-9672fd7f6fda
Khandelwal, Bhupendra
8d08c1ec-6cb3-4da9-9858-71e7b0ab3611
Antoniadou, Ifigeneia
2966f850-9ca7-4d11-8801-f75f49ad9377
Matthaiou, Ioannis
7855a890-8929-4c90-a08c-9672fd7f6fda
Khandelwal, Bhupendra
8d08c1ec-6cb3-4da9-9858-71e7b0ab3611
Antoniadou, Ifigeneia
2966f850-9ca7-4d11-8801-f75f49ad9377

Matthaiou, Ioannis, Khandelwal, Bhupendra and Antoniadou, Ifigeneia (2017) Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges. Frontiers in Built Environment.

Record type: Article

Abstract

In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by “compressing” them using the kernel principal component analysis so that more meaningful, lower-dimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM) algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and air-to-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the OCSVM, the kernel width γ and optimization penalty parameter ν. This selection was made by searching along a fixed grid space of values and choosing the combination that provided the highest cross-validation accuracy. Nevertheless, there exist challenges that are discussed along with suggestions for future work that can be used to enhance similar novelty detection schemes.

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More information

Accepted/In Press date: 29 August 2017
Published date: 20 September 2017

Identifiers

Local EPrints ID: 481794
URI: http://eprints.soton.ac.uk/id/eprint/481794
PURE UUID: bc0623c9-0612-429b-96dd-035de15f34df
ORCID for Ioannis Matthaiou: ORCID iD orcid.org/0009-0009-3603-2999

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Date deposited: 07 Sep 2023 16:46
Last modified: 18 Mar 2024 04:08

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Contributors

Author: Ioannis Matthaiou ORCID iD
Author: Bhupendra Khandelwal
Author: Ifigeneia Antoniadou

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