Centrifugal compressors are critical components in diverse power systems. However, surge phenomena pose severe risks to both the thermodynamic stability of the cycle and the mechanical integrity of the compressor. Early and accurate detection of surge inception is therefore essential for effective control and protection. This study introduces a novel surge precursor identification method based on Rényi entropy analysis of acoustic emissions. Experiments were performed on an automotive turbocharger compressor, with four microphones mounted upstream of the inlet to record acoustic signals during transient operation approaching the surge limit. Conventional Rényi entropy was found to be insufficiently sensitive to surge onset due to overfitting. To overcome this limitation, an improved Rényi entropy method was developed by reconstructing the probability function. The improved entropy exhibited a clear increasing trend prior to surge inception, effectively indicating the compressor’s approach to instability. Comparative analyses demonstrate that the improved Rényi entropy surpasses conventional entropy-based indicators—such as approximate, sample, and fuzzy entropy—in both sensitivity and robustness. To verify the general applicability of the approach, a second experiment was conducted on a fuel cell centrifugal compressor equipped with 13 inlet microphones. The improved Rényi entropy method consistently detected surge inception in this configuration as well, confirming its reliability across different compressor types. These findings highlight the potential of the improved Rényi entropy as a powerful early-warning metric for surge detection in centrifugal compressors, marking an important step toward noise-based instability monitoring and control.
VenturiMSangJKnoopA, et al. Air supply system for automotive fuel cell application. In: Presented at the SAE 2012 world congress & exhibition, April 2012. SAE International. https://doi.org/10.4271/2012-01-1225.
2.
CunninghamJMHoffmanMAFriedmanDJ. A comparison of high-pressure and low-pressure operation of PEM fuel cell systems. SAE Trans2001; 110: 464–470.
3.
LecointeBMonnierG. Downsizing a gasoline engine using turbocharging with direct injection. SAE technical paper 2003-01–0542, SAE International, March2003. https://doi.org/10.4271/2003-01-0542.
4.
KasserisEPHeywoodJB. Comparative analysis of automotive powertrain choices for the next 25 years. SAE technical paper 2007-01–1605, SAE International, April2007. https://doi.org/10.4271/2007-01-1605.
5.
WittmannTLückSBodeC, et al. Modelling the condensation phenomena within the radial turbine of a fuel cell turbocharger. Int J Turbomach Propulsion Power2021; 6(3): 23. https://doi.org/10.3390/ijtpp6030023
LiBHuangCBaiJ, et al. Circumferential propagation characteristics of initial surge wave in a centrifugal compressor. Phys Fluids2025; 37(3): 037129. https://doi.org/10.1063/5.0260471
8.
CaoYDuSZhanX, et al. Analysis of performance and aerodynamic noise of centrifugal compressor using non-uniform tip clearance at near-stall condition. Appl Acoust2025; 228: 110296. https://doi.org/10.1016/j.apacoust.2024.110296
9.
MlkvikMOlšiakRKnížatB. A method of stall recognition using nonlinear feature extraction from the compressor outlet pressure. Heliyon2023; 9(10): e20909. https://doi.org/10.1016/j.heliyon.2023.e20909
10.
ZhangHYangCZhaoB, et al. Experimental investigation of characteristics of instability evolution in a centrifugal compressor. Chin J Aeronaut2023; 36(4): 174–189. https://doi.org/10.1016/j.cja.2023.01.005
11.
YuanWLiuYYanZ, et al. An improved wiener filter-based method for identifying stall inception of transonic compressor. Aerosp Sci Technol2024; 155: 109576. https://doi.org/10.1016/j.ast.2024.109576
12.
SilvestriPNiccolini Marmont Du Haut ChampCAReggioF, et al. Vibro-acoustic responses and pressure signal analysis for early surge detection in a turbocharger compressor. J Eng Gas Turbine Power2023; 145(10): 101012. https://doi.org/10.1115/1.4063279
13.
SilvestriPReggioFNiccolini Marmont Du Haut ChampCA, et al. Compressor surge precursors for a turbocharger coupled to a pressure vessel. In: Presented at the ASME Turbo Expo 2022: turbomachinery technical conference and exposition, October 2022. American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/GT2022-81731.
14.
HouYWuPWuD. An operating condition information-guided iterative variational mode decomposition method based on mahalanobis distance criterion for surge characteristic frequency extraction of the centrifugal compressor. Mech Syst Signal Process2023; 186: 109836. https://doi.org/10.1016/j.ymssp.2022.109836
15.
StajudaMGarcía CavaDLiśkiewiczG. Aerodynamic instabilities detection via empirical mode decomposition in centrifugal compressors. Measurement2022; 199: 111496. https://doi.org/10.1016/j.measurement.2022.111496
16.
ZhangXLinZJiR, et al. Deep reinforcement learning based active surge control for aeroengine compressors. Chin J Aeronaut2024; 37(7): 418–438. https://doi.org/10.1016/j.cja.2024.03.028
17.
JinH-JZhaoY-PWangZ-Q. A rotating stall warning method for aero-engine compressor based on DeepESVDD-CNN. Aerosp Sci Technol2023; 139: 108411. https://doi.org/10.1016/j.ast.2023.108411
18.
LiBHuangCZhouT, et al. Gramian angular field imaging and texture analysis for identifying instability precursors in compressor unsteady flows. Phys Fluids2025; 37(11): 117115. https://doi.org/10.1063/5.0298010
19.
LouFKeyNL. Compressor stall warning using nonlinear feature extraction algorithms. J Eng Gas Turbine Power2020; 142(12): 121005. https://doi.org/10.1115/1.4048990
20.
ZhaoBWuXLuQ, et al. An entropy-based assessment of acoustic information confusion at unstable flow occurrence in a centrifugal compressor. Phys Fluids2025; 37(1): 016126. https://doi.org/10.1063/5.0248602
21.
ZhaoBLiBCuiW, et al. Approximate entropy identification of flow unsteadiness and surge precursor in both subsonic centrifugal and axial compressors. Phys Fluids2025; 37(2): 027144. https://doi.org/10.1063/5.0251040
22.
SandovalORMachadoLHJHanriotVM, et al. Acoustic and vibration analysis of a turbocharger centrifugal compressor failure. Eng Fail Anal2022; 139: 106447. https://doi.org/10.1016/j.engfailanal.2022.106447
23.
PanTYanZYanJ, et al. Experimental investigation of instability inception on a transonic compressor under various radial distributions of loading. Aerosp Sci Technol2023; 134: 108167. https://doi.org/10.1016/j.ast.2023.108167
24.
PerovicDHallCAGunnEJ. Stall inception in a boundary layer ingesting fan. J Turbomach2019; 141(9): 091007. https://doi.org/10.1115/1.4043644
25.
KameierFNeiseW. Rotating blade flow instability as a source of noise in axial turbomachines. J Sound Vib1997; 203(5): 833–853. https://doi.org/10.1006/jsvi.1997.0902
TengCHomcoS. Investigation of compressor whoosh noise in automotive turbochargers. SAE Int J Passeng Cars Mech Syst2009; 2(1): 1345–1351. https://doi.org/10.4271/2009-01-2053
28.
VaculaJNovotnýP. An overview of flow instabilities occurring in centrifugal compressors operating at low flow rates. J Eng Gas Turbine Power2021; 143(11): 111002. https://doi.org/10.1115/1.4051642
29.
EvansDWardA. Minimising turbocharger whoosh noise for diesel powertrains. In: Presented at the SAE 2005 noise and vibration conference and exhibition, May 2005. SAE International. https://doi.org/10.4271/2005-01-2485.
30.
BroatchAGalindoJNavarroR, et al. Numerical and experimental analysis of automotive turbocharger compressor aeroacoustics at different operating conditions. Int J Heat Fluid Flow2016; 61: 245–255. https://doi.org/10.1016/j.ijheatfluidflow.2016.04.003
31.
TorregrosaAJBroatchANavarroR, et al. Acoustic characterization of automotive turbocompressors. Int J Engine Res2015; 16(1): 31–37. https://doi.org/10.1177/1468087414562866
KumarRSinghSBilgaPS, et al. Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: a critical review. J Mater Res Technol2021; 10: 1471–1492. https://doi.org/10.1016/j.jmrt.2020.12.114
RichmanJSMoormanJR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol2000; 278(6): H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
39.
ChenWWangZXieH, et al. Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst Rehabil Eng2007; 15(2): 266–272. https://doi.org/10.1109/TNSRE.2007.897025
