利用声场空间分布特征诊断滚动轴承故障

基于振动信号分析的特征提取是目前最主要的机械故障诊断方法,而振动信号的获取受到接触式测量的限制,基于声学测量的故障诊断能够克服这一缺点,但传统基于单通道测试的声学诊断技术存在测点选择难和局部诊断的不足。基于近场声全息技术提出一种用于滚动轴承故障诊断的声场分布特征提取方法。不同轴承故障能产生不同的振动特性,进而产生相应的声场分布,鉴于轴承状态与声场分布特性的对应关系,利用近场声全息算法重建声源附近各轴承运行状态下的声场,得到反映声场分布的二维声像图,再从声像图中提取故障相关的灰度共生矩阵特征,建立声场分布特性与轴承运行状态间的内在联系,结合支持矢量机模式分类,用于轴承的故障诊断。研究表明所提出的声场分布特征提取方法能够有效地用于滚动轴承的各类故障诊断,为机械故障诊断提供了新的参考。

Diagnosing Rolling Bearing Faults Using Spatial Distribution Features of Sound Field

The vibration-based feature extraction is the main approach for mechanical fault diagnosis,whereas,in some conditions vibration signal is not easily measured because of its contact-measuring.Acoustic-based diagnosis(ABD) can overcome this disadvantage.However,for traditional ABD it is hard to choose proper measuring positions and the acoustic signals acquired based on single channel measurement can be used only for local analysis.Based on near-field acoustic holography(NAH),a new feature extraction method by using sound field distribution for rolling bearing fault diagnosis is presented.Firstly,sound fields in different bearing conditions are reconstructed by NAH.Using gray level co-occurrence matrix(GLCM) features extracted from acoustic images,the inner relationship between bearing conditions and sound fields is established.These features are fed into support vector machine(SVM) classifier for fault diagnosis.The effectiveness of our proposed method is demonstrated on the experimental investigation.The method provides a new reference for mechanical fault diagnosis.