基于特征基的GMC卷积稀疏机械故障特征解析方法

Feature-based GMC convolutional sparse representation method for mechanical fault feature resolution

在机械设备的复杂工况下,监测信号易受多振动源及环境噪声干扰,导致故障特征微弱且呈现强耦合特性,这给设备故障诊断带来极大挑战。因此,提出了一种基于振动特性基的GMC增强卷积稀疏机械故障特征解析方法,实现微弱耦合故障特征解析。首先,构造了一种自适应单边衰减小波匹配算法以获取最优特征原子,将最优特征原子升维同时匹配故障周期,以得到具有周期特征的振动特征基。其次,提出基于GMC增强的卷积稀疏编码,结合振动特征基优化求解稀疏系数。此外,提出了一种基于平均峭度与谐波能量比的过程参数优化选择方法,克服了优化过程中关键参数难选取的问题。最后,提取包络谱主要特征与理论故障特征频率对比判断故障类型。通过仿真分析和试验台信号验证,并对比分析了基于谱峭度分解和可调变Q因子小波变换GMC稀疏增强等两种传统方法。实验结果表明,相较于上述两种传统方法,本文提出的方法可以有效地分离不同类型的故障特征信号,并实现故障特征的增强。

In complex working conditions,the monitoring signals of mechanical equipment are easily disturbed by multi-vibration sources and background noise,making weak fault features and strong coupling.It brings a great challenge to fault diagnosis.Therefore,a generalized minimax-concave enhanced convolutional sparse mechanical fault features resolution method based on the vibration characteristics atom is proposed to analyze weak features and strong-coupling faults.Firstly,an auto-adapted single-side fading wavelet framework is constructed to obtain the optimal feature atoms.The optimal feature atoms are increased in dimension to match the fault periodic to get the vibration feature atoms with periodic characteristics.Secondly,a convolutional sparse coding method based on GMC enhancement is proposed,which combines vibration feature atoms to obtain the sparse coefficients optimally.In addition,a processing parameter optimal selection method based on the ratio of average kurtosis to harmonic energy is designed,which overcomes the dilemma of selecting key parameters.Finally,the main features of the envelope spectrum are extracted and compared with theoretical fault feature frequencies to determine fault type.The effectiveness and superiority of the proposed method are verified by simulated and real test-bed signals.The spectrum kurtosis and tunable Q-factor wavelet transform Generalized Minimax-concave sparse enhancement method are set as comparison groups.The results demonstrate that different fault features are better decoupled,and the sparse component amplitudes are well improved compared to the comparison method.