Novelty detection based on extensions of GMMs for industrial gas turbines

Zhang, Yu, Bingham, Chris, Gallimore, Michael and Cox, Darren (2015) Novelty detection based on extensions of GMMs for industrial gas turbines. In: IEEE international conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 12-14 June, 2015, Shenzhen, China.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


The paper applies the application of Gaussian mixture models (GMMs) for operational pattern discrimination and novelty/fault detection for an industrial gas turbine (IGT). Variational Bayesian GMM (VBGMM) is used to automatically cluster operational data into steady-state and transient responses, where extraction of steady-state data is an important pre-processing scenario for fault detection. Important features are extracted from steady-state data, which are then fingerprinted to show any anomalies of patterns which may be due to machine faults. Field data measurements from vibration sensors are used to show that the extensions of GMMs provide a useful tool for machine condition monitoring, fault detection and diagnostics in the field. Through the use of experimental trials on IGTs, it is shown that GMM is particularly useful for the detection of emerging faults especially where there is a lack of knowledge of machine fault patterns.

Keywords:Gaussian mixture model, operational pattern discrimination, feature extraction, novelty detection, industrial gas turbine
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Engineering
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ID Code:17831
Deposited On:09 Jul 2015 09:06

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