The rotating stall inception data analysis using Analytic Wavelet Transform (AWT) in a low-speed axial compressor was presented in the authors’ previous studies , . These studies focused on the detection of instability inception in an axial flow compressor when it enters into the instability regime due to the modal type of stall perturbation. In this paper, the effectiveness of AWT is further studied by applying it under different testing conditions. In order to examine the results of AWT on highly sampled data, at first, the stall data were acquired at a high sampling frequency and the results were compared with the conventional filtered signals. Secondly, the AWT analysis of stall data was carried out for the condition when compressor experienced a spike type rotating stall disturbance. The stall inception information obtained from the AWT analysis was then compared with the commonly used stall detection techniques. The results show that AWT is equally beneficial for the diagnostic of compressor instability regardless of the data sampling rate and represents an outstanding ability to detect stall disturbance irrespective of the type of stall precursor, i.e. the modal wave or spike.
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