Neural networks and simple models for the fault diagnosis of naval turbochargers
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Guide to Neural Computing Applications
Guide to Neural Computing Applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Learning shape for jet engine novelty detection
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Support vector machine in novelty detection for multi-channel combustion data
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Application of an intuitive novelty metric for jet engine condition monitoring
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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We develop novelty detection techniques for the analysis of data from a large-vehicle engine turbocharger in order to illustrate how abnormal events of operational significance may be identified with respect to a model of normality. Results are validated using polynomial function modelling and reduced dimensionality visualisation techniques to show that system operation can be automatically classified into one of three distinct state spaces, each corresponding to a unique set of running conditions. This classification is used to develop a regression algorithm that is able to predict the dynamical operating parameters of the turbocharger and allow the automatic detection of periods of abnormal operation. Visualisation of system trajectories in high-dimensional space are communicated to the user using parameterised projection techniques, allowing ease of interpretation of changes in system behaviour.