Structured Bayesian sparse representation of aero-engine bearing failures

ZHANG Shuo; LIU Zhiwen

doi:10.7527/S1000-6893.2021.25056

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2022 , Vol. 43 >Issue 9: 625056 - 625056

DOI: https://doi.org/10.7527/S1000-6893.2021.25056

Special Topic: Operation Safety of Aero-engine

Structured Bayesian sparse representation of aero-engine bearing failures

ZHANG Shuo ,
LIU Zhiwen

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School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received date: 2020-12-04

Revised date: 2020-12-30

Online published: 2021-04-29

Supported by

National Natural Science Foundation of China (52075080, U1733107); Aeronautical Science Foundation of China (20173319003)

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Abstract

The instantaneous impact component associated with the fault in the aero-engine bearing vibration signal has not only sparseness in the time-frequency transform domain but also specific structural characteristics. Traditional greedy algorithms and their improved reconstruction methods, represented by the Orthogonal Matching Pursuit (OMP) algorithm, usually only use the sparseness of the signal as a whole, but not the possible impact of structural interference, resulting in lower solution efficiency. This paper proposes a structured Bayesian sparse representation method for the parameter optimization dictionary. First, on the basis of the OMP algorithm, a Structured Bayesian Orthogonal Matching Pursuit (SBOMP) sparse representation model is developed based on the Bayesian probability model to promote the sparse reconstruction effect, so as to obtain the sparse representation of signals. Second, considering the characteristics of bearing fault vibration signals, a time-frequency impulse atomic library that can better match the analysis signals is constructed to reduce redundancy of the dictionary. The Grey Wolf Optimization (GWO) algorithm is introduced into the SBOMP model based on the time-frequency impulse dictionary to provide an efficient atom selection strategy for the SBOMP model and reduce complexity of the sparse model. Simulation and experimental results show that the method proposed in this paper can more effectively reduce interference of background noise and impurity frequencies, which verifies the effectiveness and applicability of the method proposed for extraction of aero-engine bearing fault features.

Key words： fault diagnosis; structured Bayesian orthogonal matching tracking; gray wolf optimization algorithm; time-frequency impact dictionary; aero-engine bearing

Cite this article

ZHANG Shuo , LIU Zhiwen . Structured Bayesian sparse representation of aero-engine bearing failures[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(9) : 625056 -625056 . DOI: 10.7527/S1000-6893.2021.25056

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