基于RSSD和小波变换的滚动轴承故障诊断

Rolling Bearing Fault Diagnosis Method Based on Resonance-Based Sparse Signal Decomposition and Wavelet Transform

滚动轴承故障被视作瞬态冲击成分,在信号共振稀疏分解中一般被分解到的低共振分量当中。由于噪声影响,低共振分量的希尔伯特解调包络谱中依然存在大量的干扰频率,使得故障特征提取有时不明显,或不易观察,因此本文提出了一种基于信号共振稀疏分解(RSSD)与小波变换相结合的故障诊断方法。在滚动轴承早期微弱故障的诊断中,采用小波分析技术对隐藏于低共振分量的故障特征进行提取,可以更加有效地凸显故障特征;通过对滚动轴承内圈和外圈单一故障振动信号的分析应用,成功提取了故障特征,验证了这一方法在滚动轴承早期故障诊断应用的有效性。

Rolling bearing faults are considered as transient shock components, which are generally decomposed into low resonance components in signal resonance sparse decomposition. A large number of interference frequencies still exist in the Hilbert demodulation envelope spectrum of low resonance components due to the influence of noise,which makes the fault feature extraction unobvious or hard to observe. Therefore,a fault diagnosis method based on resonance-based sparse signal decomposition and wavelet transform was proposed. In the early diagnosis of weak faults of rolling bearings,the fault features hidden in the low-resonance components were extracted by wavelet analysis technology to highlight the fault features more effectively.Through the analysis and application of the single fault vibration signal of the inner and outer rings of the rolling bearing,the fault characteristics were extracted successfully,and the validity of this method in the early fault diagnosis of the rolling bearing was verified.