Rotating stall analysis using signal-adapted filter bank and Cohen´s time-frequency distributions

We examine the promising approach of using Time-Frequency Distributions (TFDs) based on Cohen´s class to characterize the unstable operation (stall) of an axial compressor. Stall precursors are time-localized transients which indicate a coming stall inception. Our approach uses the acoustic vibration signal to reveal the nonstationary behavior when approaching the stall region. The results in this paper show that it is possible to visualize stall precursors by relying on the microphone signal which represents a low cost setup compared to the use of a dynamic pressure probe array. To overcome several drawbacks of standard Fourier scheme, we propose the use of a signal-adapted filter bank and TFD based on Cohen´s class to achieve enhanced signatures while reducing efficiently the computational requirements