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, lowerdimensional 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 airto-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

Management of KPC-Producing Klebsiella pneumoniae Infections

Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae (KPC-KP) has become one of the most important contemporary pathogens, especially in endemic areas

Towards a condition monitoring scheme for combustion instability detection and fuel blends performance classification in gas turbine engines using pattern recognition and advanced machine learning

The investigation and improvement in fuel performance and combustion is necessary in order to minimize emissions and operation costs in various engineering applications e.g. aerospace. Among these factors, nevertheless, ensuring safe operation is a priority: undesired phenomena, such as thermoacoustic instabilities, can have detrimental effects on jet engines, gas turbines and combustors, in general, due to excessive vibrations. It is for this reason that monitoring and design schemes should be able to identify the potential of occurrence of such events. This is a difficult task due to the complexity of the nature of these events. This paper is a preliminary investigation into the performance and characterization of various fuel blends and the examination of the vibration levels expected for different combustion states of a gas turbine engine. We tackle the issue from the perspective of modifying the input to the system (i.e. the fuel composition) in order to investigate nonlinear behavior of the gas turbine engine through the development of a multi-class classification algorithm. Features from a vibration channel for each of the fuel blends were extracted for both classification modelling and cluster analysi

Using Gaussian Processes to model combustion dynamics

Modelling the dynamics of combustion is a challenging task due to the non-linear interaction of many processes involved, including chemical kinetics, flame dynamics and acoustic pressure variations inside the chamber. Given that gas turbine engines are the dominant power generation sources, more sophisticated models that can make accurate and reliable predictions regarding the combustion processes and its efficiency, are always in high demand. This paper discusses the development of a data-driven model that is based purely on experimental data, collected from a combustion test rig. The model has been developed using Gaussian Processes, an advanced Bayesian non-parametric machine learning algorithm. The collected data, including pressure inside the combustion primary zone and structural vibration, were all considered as possible candidates for adapting this algorithm to the dynamical characteristics of the combustion chamber under investigation. Accuracy in prediction using this empirical model was investigated for different combinations of experimental data and fractions of them, using the root mean squared error as performance measure. The covariance function parameters of the Gaussian Process model were optimised using a gradient-based algorithm for the best possible adaptation to the experimental dataset