Sound signature analysis using time-frequency signal processing: Application to active stall avoidance in axial compressors

We examine the promising approach of using Time-Frequency Analysis 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. The analysis uses the acoustic vibration signal to reveal the non-stationary 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 dynamic pressure probe array. The design goal of an active stall avoidance system in the future requires us to consider already at the algorithm design an effective computing scheme. We adopt the approach of representing the discrete Time-Frequency Distribution (TFD) as a sum of spectograms which is advantageous for real-time implementation issues of the highly complex algorithm. Approximation is possible by taking only significant terms of the decomposition into account. The result of this work serves as a preprocessing step for the later design of a detection/classification system.