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.

This content is only available via PDF.
You do not currently have access to this content.