Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

Abstract

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.