A conceptual study of utilizing compressive-sensing-based fan noise mode detection for aeroengine prognostic and health management

2020 ◽  
Vol 148 (2) ◽  
pp. 1063-1076
Author(s):  
Huanxian Bu ◽  
Xun Huang ◽  
Xin Zhang
Keyword(s):  
Compressive Sensing ◽  
Health Management ◽  
Fan Noise ◽  
Mode Detection ◽  
Conceptual Study ◽  
Prognostic And Health Management

Compressive Sensing Approach for Aeroengine Fan Noise Mode Detection

2018 ◽  
Author(s):  
Huanxian Bu ◽  
Wenjun Yu ◽  
Xun Huang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Wind Tunnel Test ◽  
Test Method ◽  
Fan Noise ◽  
Noise Interference ◽  
Mode Detection ◽  
Flow Speeds ◽  
Noise Test ◽  
Set Up

To further simplify the sensor array set-ups and improve the mode detection capability for the aeroengine fan noise test, a new compressive sensing based methodology has been proposed. This paper reports the details of the validated aeroengine fan noise test method and the wind tunnel test results for the validation. The experimental set-up consists of a transition duct to the open jet, a mode synthesizer to generate different modes of characteristic fan noise, and a sensor array to conduct mode detection in the presence of background flow speeds and background noise interference. The main attention is primarily focused on the examination of the associated reconstruction accuracy and probability of success for spinning mode detection. The testing results clearly show the potential capability of the proposed new testing method for aeroengine tests in a practical testing facility.

Nonconvex L1/2 Minimization Based Compressive Sensing Approach for Duct Azimuthal Mode Detection

AIAA Journal ◽  
2020 ◽  
Vol 58 (9) ◽  
pp. 3932-3946
Author(s):  
Baohong Bai ◽  
Xiaodong Li ◽  
Tao Zhang ◽  
Dakai Lin
Keyword(s):  
Compressive Sensing ◽  
Azimuthal Mode ◽  
Mode Detection

scholarly journals Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 517 ◽  
Author(s):  
Yunfei Ma ◽  
Xisheng Jia ◽  
Qiwei Hu ◽  
Daoming Xu ◽  
Chiming Guo ◽  
...  
Keyword(s):  
Fault Detection ◽  
Compressive Sensing ◽  
Health Management ◽  
Signal Transmission ◽  
Vibration Signal ◽  
Experimental Tests ◽  
Reconstructed Signal ◽  
Signal Simulation ◽  
Value Decomposition ◽  
Laplace Priors

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests.

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