Bi-regularization enhanced azimuthal mode analysis method for the aero-engine fan. (15th May 2022)

• Record Type: Journal Article
• Authors: Li, Zepeng; Qiao, Baijie; Wen, Bi; Liu, Junjiang; Chen, Xuefeng
• Is Part Of: Mechanical systems and signal processing. Volume 171(2022)
• Publication Date: 2022-05-15
• Subjects: Azimuthal mode analysis -- Lp-norm -- Tikhonov regularization -- Compressive sampling; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108921
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Azimuthal mode analysis is regarded as an effective tool for aero-engine flow field investigation, where numerous microphones are evenly mounted as a ring to provide sufficient spatial resolution for azimuthal pressure acquisition. The current compressive sampling method enables azimuthal modes to be obtained from much fewer microphones, with dominant and non-dominant modes separately estimated by L1-norm regularization and spatial Fourier transform or least square method. Despite the effort of measurement system simplification, this classical compressive sampling method regularized by L1-norm inherently introduces accuracy loss in amplitude estimation for both dominant and non-dominant modes. With the aim of accuracy promotion and further measurement reduction, the Bi-regularization enhanced azimuthal mode analysis (BRAMA) method is devised to investigate the azimuthal modes of the aero-engine fan via limited acoustic measurements. The BRAMA method substitutes L1-norm regularization with L p -norm ( 0 < p < 1 ) regularization to reach more accurate reconstruction of dominant modes. Meanwhile, the Tikhonov regularization based on L2-norm is introduced to estimate non-dominant modes. The effectiveness of the BRAMA method is verified in two cases, by operating a 2.5-stage aero-engine fan test rig at 50% and 90% of the nominal speed, respectively. Firstly, acoustic pressure signals are pre-processed to extract the dominant tonal component, which are used as the input of dominant mode estimation. Next, comparative realizations by L1-norm and L p -norm are conducted to detect the dominant modes, meanwhile the amplitude accuracy with respect to p value and measurement number is discussed in both cases. Finally, non-dominant modes are estimated by using Tikhonov regularization in comparison to the classical approaches. Experimental results of both cases indicate that the BRAMA method outperforms the classical compressive sampling approach in accuracy improving, measurements reducing and interference proof capability. Highlights: The L p -norm (0< p <1) regularization is introduced estimate the dominant modes. The Tikhonov regularization is utilized to estimate the non-dominant modes. Necessary preprocessing based on cyclostationary theory is conducted to extract the tonal component from the acoustic series. The effectiveness of the proposed BRAMA method is verified on a 2.5-stage aero-engine acoustic testing system, with the rotor operating at various speeds. Results demonstrate that BRAMA method outperforms the classical methods in accuracy improvement.