Automated Gas Turbine Sensor Fault Diagnostics

Gbanaibolou Jombo, Yu Zhang, Jonathan Griffiths, Anthony Latimer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Sensors provide a means of detecting the actual operating condition of the gas turbine at any point in time. As such, detecting when they exhibit anomalous behaviour is very important to prevent inaccurate performance predictions and costly gas turbine component failures. This paper presents an automated sensor fault detection approach based on the maximal overlap discrete wavelet transform for the detection of the presence of anomalies such as bias, spike, stuck signal, cross-talk and erratic fault in a sensor signal. A multi-sensor validation scheme based on using the median signal as a reference signal within a sensor group is shown to be a robust technique to differentiate sensor anomalies due to gas turbine transient operation from actual sensor faults. The approach presented in this paper lends itself not only suitable for gas turbines in service, but also for gas turbines undergoing acceptance testing. As the timescales involved during a gas turbine acceptance test are just in hours, the conventional approach of trending and setting alarm limits are not sensitive to sensor anomalies which occur within the set alarm limits. Wavelet-based approach combined with a multi-sensor validation scheme provides a viable alternative.
Original languageEnglish
Title of host publicationASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition
Subtitle of host publicationCeramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy
Number of pages10
Publication statusPublished - 11 Jun 2018
EventASME 2018 Turbo Expo, Turbomachinery Technical Conference & Exposition - Oslo, Norway
Duration: 11 Jun 2018 → …


ConferenceASME 2018 Turbo Expo, Turbomachinery Technical Conference & Exposition
Period11/06/18 → …


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