Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

• Main Authors: Yu Zhang, Chris Bingham, Miguel Martínez-García, Darren Cox
• Format: Article
• Language: English
• Published: Hindawi Limited 2017-01-01
• Series: International Journal of Rotating Machinery
• Online Access: http://dx.doi.org/10.1155/2017/5435794

This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.