【摘要】 基于Q因子的稀疏分解是信号的一种自适应稀疏化表达方法。针对强噪声环境下齿轮箱非平稳复合故障信号难于提取与分离的问题,提出基于并联双重Q因子的快速独立分析方法。首先通过基于并联双重Q因子的小波变换分析方法对单通道故障信号进行降噪和升维处理,根据不同的低Q因子值得到多组低共振的冲击成分,组成多维信号,实现信号升维,然后应用快速独立分析方法进行盲分离。仿真信号数据分析结果及滚动轴承复合故障的实验数据分析结果均表明了该方法的可行性和有效性,为强噪声环境下的复合机械故障信号分离与提取提供了一种新的思路。更多还原

【Abstract】 The sparse decomposition based on Q-factor is an adaptive sparse expression method for signals.Aiming at the problem that the gearbox non-stationary composite fault signal submerged in strong noise environment is difficult to be extracted and segmented,a fast independent component analysis method based on the parallel dual-Q-factors is proposed.Firstly,the wavelet transform analysis method based on the parallel dual-Q-factors is used to perform the denoising and dimension raising of the single-channel mechanical fault signal,many low-resonance impact components are obtained according to different Q-factors,which make up multi-dimension signal and signal dimension-raising is achieved.Secondly,the fast independent component analysis method is used to carry out the blind separation of the composite fault signals.The data analysis results of simulation signal and the analysis results of the experiment data for the roller bearing composite faults confirm the feasibility and validity of this method.The proposed method provides a new idea for the separation and extraction of the mechanical composite fault signals in strong noise environment.更多还原