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

Probabilistic outlier detection in vibration spectra with small learning dataset

The issue of detecting abnormal vibrations from spectra is addressed in this article, when little is known both on the mechanical behavior of the system, and on the characteristic patterns of potential faults. With vibration measured from a bearing test rig and from an aircraft engine, we show that when only a small learning set is available, probabilistic approaches have several advantages, including modelling healthy vibrations, and thus ensuring fault detection. To do so, we compare two original algorithms: the first one relies on the statistics of the maximum of log-periodograms. The second one computes the probability density function (pdf) of the wavelet transform of log-periodograms, and a likelihood index when new periodograms are presented. A by-product of it is the ability to generate random log-periodograms according with respect to the learning dataset. Receiver Operator Characteristic (ROC) curves are built in several experimental settings, and show the superiority of one of our algorithms over state-of-the-art machine-learning-oriented fault detection methods; lastly we generate random samples of aircraft engine log-periodograms

Automated data inspection in jet engines

Rolls Royce accumulate a large amount of sensor data throughout the testing and deployment of their engines. The availability of this rich source of data offers exciting opportunities to automate the monitoring and testing of the engines. In this thesis we have developed statistical models to make meaningful insights from engine test data. We have built a classification model to identify different types of engine running in Pass-Off tests. The labels can be used for post-analysis and highlight problematic engine tests. The model has been applied to two different types of engines, in which it gives close to perfect classification accuracy. We have also created an unsupervised approach when there are no defined classes of engine running. These models have been incorporated into Rolls Royce systems. Early warnings for potential issues can enable relatively cheap maintenance to be performed and reduce the risk of irreparable engine damage. We have therefore developed an outlier detection model to identify abnormal temperature behaviour. The capabilities of the model are shown theoretically and tested on experimental and real data. Lastly, in a test decisions are made by engineers to ensure the engine complies with certain standards. To support the engineers we have developed a predictive model to identify segments of the engine test that should be retested. The model is tested against the current decision making of the engineers, and gives good predictive performance. The model highlights the possibility of automating the decision making process within a test

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles

Brief Review of Vibration Based Machine Condition Monitoring

In the process of channeling energy into job to be performed all machines vibrate. Machines rarely break down without giving some previous warning. The signs of impeding failure are generally present long before a machine totally breaks down. When faults begin to develop in the machine, some of dynamic processes in the machine are changed as well, thereby influencing machine vibration level, temporal and spectral vibration properties. Such changes can act as an indicator for early detection and identification of developing faults. This paper briefly reviews the machine condition monitoring based on vibration data analysis. After the review of major, well established and mature approaches, new unsupervised approaches based on novelty detection are also briefly mentioned

The Concorde and aeronautical research

Theoretical and experimental work carried out in various research centers, and particularly at ONERA, which led to the conception and to the main technical solutions included in the design of Concorde: plane form, twist and camber of the wing, lift augmentation by upper surface vortices, kinetic heating, air intakes and jet exhausts, materials, aeroelasticity. The development of research, and the numerous tests carried out for the benefit of the designers since the beginning of the project, are also outlined