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

• Published in: International Journal of Rotating Machinery Vol. 2017; no. 2017; pp. 1 - 9-027
• Main Authors: Cox, Darren, Martínez-García, Miguel, Bingham, Chris, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2017; Hindawi Publishing Corporation; Hindawi; Hindawi Limited
• Subjects: Failure; False alarms; Feasibility studies; Gas turbines; Methods; Noise; Principal components analysis; Shutdowns; Signal processing
• ISSN: 1023-621X; 1542-3034
• DOI: 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.