Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machines efficiency and in severe cases leading to failures of the compressing system due to high amplitude vibrations. Efficient instabilities detection during compressor operation is a challenge of utmost importance from economical and safety point of view. The most sensitive detection is possible with use of a pressure signal from inside of the compressor because specific pressure patterns are the first symptoms of instabilities. Detection of aerodynamic instabilities results in specific challenges, as the pressure signal is often very noisy and contains high amount of randomness. Surge – most severe instability, can develop very quickly. Therefore, the method of detection should be sensitive but also robust and quick. Another common instability, inlet recirculation is less dangerous, but it results in decrease of efficiency, which is to be avoided. Inlet recirculation often happens before surge, thus its presence can be used for surge proximity detection. The aim of this study is to investigate and compare the performance of two non-linear processing methods - Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA) in the context of aerodynamic instabilities detection – inlet recirculation and surge. The comparison focuses on the robustness, sensitivity and pace of detection – crucial parameters for a successful detection method. It is shown that both methods perform similarly within the analyzed bounds for both instabilities. A slight advantage of SSA may be noticed for surge due to lower dispersion of the indicator value for the same conditions.
|Title of host publication||2021 Signal Processing Symposium (SPSympo)|
|Publication status||Published - 15 Nov 2021|