基于稀疏分解的轴承声阵列信号特征提取
Microphone Array Signal Feature Extraction of Bearings Based on Sparse Decomposition

利用滚动轴承运行时的异常声响来识别轴承故障，搭建了轴承声阵列信号故障诊断实验平台。针对轴承声信号信噪比差、成分复杂、故障特征不明显的特点，提出一种基于稀疏分解的轴承传声器阵列信号特征提取方法。利用全息面有效声压场及其投影图对实验设备进行噪声源识别与定位，通过coif4小波字典和局部余弦字典构建冗余字典，采用稀疏分解提取热点噪声源声信号的冲击特征。仿真和实际声信号的处理结果表明，该方法准确提取了不同转速下声信号中的故障特征频率，证明了利用声阵列信号对轴承进行故障识别的有效性和可靠性。
In order to identify the fault of bearing through abnormal sound caused by the running bearing, an experimental platform for acoustic signal fault diagnosis of bearing is established. In the light of the poor signal-to-noise ratio, complex composition and weak fault feature of the acoustic signal, a microphone array signal feature extraction method of bearings is proposed based on sparse decomposition. The noise sources are identified and located through effective sound pressure fields and projections of holographic planes. Redundant dictionary is composed of coif4 wavelet and local cosine dictionaries. The impact features of sources′acoustic signal are extracted by sparse decomposition. The processing results of simulated and actual acoustic signals show that feature frequencies of fault acoustic signals under different rotating speed of bearing are accurately extracted by the proposed method, demonstrating the effectiveness and reliability of bearing fault identification through the microphone array signal.