Gas-path measurements used to assess the health condition of an engine are corrupted by noise. Generally, a data cleaning step occurs before proceeding with fault detection and isolation. Classical linear filters such as the EWMA filter are traditionally used for noise removal. Unfortunately, these low-pass filters distort trend shifts indicative of faults, which increases the detection delay. The present paper investigates two new approaches to nonlinear filtering of time series. On the one hand, the synthesis approach reconstructs the signal as a combination of elementary signals chosen from a predefined library. On the other hand, the analysis approach imposes a constraint on the shape of the signal (e.g., piecewise constant). Both approaches incorporate prior information about the signal in a different way, but they lead to trend filters that are very capable at noise removal while preserving at the same time sharp edges in the signal. This is highlighted through the comparison with a classical linear filter on a batch of synthetic data representative of typical engine fault profiles.
Analysis Versus Synthesis for Trending of Gas-Path Measurement Time Series
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 14, 2014; final manuscript received July 18, 2014; published online September 16, 2014. Editor: David Wisler.
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Borguet, S., Léonard, O., and Dewallef, P. (September 16, 2014). "Analysis Versus Synthesis for Trending of Gas-Path Measurement Time Series." ASME. J. Eng. Gas Turbines Power. February 2015; 137(2): 022603. https://doi.org/10.1115/1.4028385
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