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

Ioannis Matthaiou,Ifigeneia Antoniadou,Bhupendra Khandelwal

doi:10.3389/fbuil.2017.00054

Abstract

In this paper, 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 nonstationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the nonstationary 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 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 one-class support vector machine, 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.

Highlights

Vibration measurements are commonly considered to be a sound indicator of a machine’s overall health state
The radial basis function (RBF) kernel is one of the most popular ones, since it implies general smoothness properties for a dataset, an assumption that is commonly accepted in many real-world applications, as discussed in more detail in Scholkopf and Smola (2001)
The novelty detection scheme was chosen over a classification approach due to the lack of training data for the various states of an engine’s operation, commonly faced in real life applications

Summary

Introduction

Vibration measurements are commonly considered to be a sound indicator of a machine’s overall health state (global monitoring). The general principle behind using vibration data is that when faults start to develop, the system dynamics change, which results in different vibration patterns from those observed at the healthy state of the system monitored. Gas turbine engine manufacturers have turned their attention into increasing the reliability and availability of their fleet using data-driven vibrationbased condition monitoring approaches (King et al, 2009). These methods are generally preferred, for online monitoring strategies, over a physics-based modeling approach, where a generic theoretical model is developed and in which several assumptions surround its development. Engine manufacturers see the need to implement such approaches during pass-off tests, where it is necessary to identify possible defects at an early stage, before complete component failure occurs

Methods

Results

Conclusion