An entropy-based assessment of acoustic information confusion at unstable flow occurrence in a centrifugal compressor

Centrifugal compressors play a crucial role in energy and power systems, and compressor surge significantly impact their safe operation. Monitoring typical surge inception characteristics in a compressor can effectively serve as an early warning indicator, thereby enhancing safety. This paper presents the results of an acoustic experiment conducted on a centrifugal compressor setup. Four microphones were positioned in front of the compressor inlet to record the noise emitted as the compressor entered its surge region from a near-surge state. An entropy-based method is proposed to extract surge inception from acoustic signals by evaluating the informational complexity. Four types of entropy, including approximate entropy, sampling entropy, fuzzy entropy, and information entropy, were calculated from the acoustic signals. Their time-domain curves exhibit a clear increasing trend prior to surge inception. Further analysis reveals differing growth rates of the entropy curves among the four microphones, confirming the impact of microphone position on the capture of flow information and, consequently, on surge warning accuracy. Eventually, an entropy-weight method is proposed to eliminate the effects of positions and enhance surge warning capabilities by accounting for multiple acoustic signals.