基于稀疏表示的轴承早期故障特征提取

低速重载机械设备中的滚动轴承由于承受巨大载荷,极易出现内外环故障. 在故障早期阶段,反映故障特征的冲击成分很微弱,极易被噪声覆盖而难以识别. 为准确诊断轴承早期故障,提出基于稀疏表示的故障特征提取方法. 该方法利用K-SVD字典训练算法构造出能准确匹配冲击成分的字典,克服了参数化字典缺乏自适应性的问题;稀疏编码过程中,采用批处理正交匹配追踪算法(batch orthogonal matching pursuit,Batch-OMP)对振动信号进行分解,以逼近信号的峭度值最大原则作为分解结束条件,自适应确定出分解次数;最后,通过对重构的特征成分进行包络谱分析得出故障类型. 对仿真信号和轴承振动信号进行故障特征提取,结果表明所提方法能准确提取出冲击成分,验证了其有效性和实用性. 

Initial Fault Feature Extraction of Bearing Based on Sparse Representation

Rolling bearings of low-speed and heavy-duty machinery work under huge load, therefore they are easily gotten inner or outer race faults. In initial fault stage, the impulse component, reflecting the fault feature in vibration signal, is difficult to extract for it is relatively weak and easily corrupted by strong background noise. A fault feature extraction method based on sparse representation was proposed to accurately diagnose the initial fault of bearing. The method utilized K-SVD dictionary training algorithm for constructing an accurate dictionary to match the impulse component and overcome the problem of parameter dictionary lack of adaptability. In sparse coding, batch orthogonal matching pursuit (Batch-OMP) algorithm was employed to sparse-decompose the vibration signal, and the kurtosis maximum principle of approximation signal was the end condition of decomposition, which determined the decomposition times adaptively. Finally, the feature component was reconstructed and its envelope spectrum was analyzed to diagnose the fault type. The fault feature was extracted by the proposed method from simulate and bearing vibration signals. The results show that the method can extract the impulse components accurately, which demonstrates its effectiveness and practicability.