Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors

Abstract

Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods—Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)—for aerodynamic instability detection. Two instabilities of different character, local—inlet recirculation and global—surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection—crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection.

Aerodynamic instabilities detection via empirical mode decomposition in centrifugal compressors

“…In this section the IMF processing method used in this study is presented and all its key parameters are defined for the centrifugal compressor presented in section 4. The selection of key values is an important step, as EMD parameters in conjunction with signal length can influence the performance decomposition in highlighting and extracting features of a signal [42].…”

“…Due to the use of quasidynamic measurements, the window-based approach was used as it allows to perform structured analysis of differences between operating conditions. The choice of a window length for decomposition is an important aspect that may affect the robustness and responsiveness of the method [42]. For the presented study, the analysis was done for the window length of ten revolutions of the impeller, which corresponds to 0.1 seconds for this machine.…”

Application of singular spectrum analysis to nonstationary time series in flow-induced vibrations of a circular cylinder

“…One of the main advantages of SSA is that only two parameters, namely the window length L and the number of components r, are required to analyze the time series. The choice of these two parameters are subjective and the selection is data and analysis dependent (Logan et al 2021, Stajuda et al 2022. However, the values of L and r could be determined according to the information of the available time series.…”