关键词: 故障诊断, 结构化贝叶斯正交匹配追踪, 灰狼优化算法, 时频冲击字典, 航空发动机轴承

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