信号稀疏分解理论在轴承故障检测中的应用  

Application of signal sparse decomposition theory in bearing fault detection

作　　者：张新鹏[1] 胡茑庆[1] 程哲[1] 胡雷[1] 陈凌[1] 

机构地区：[1]国防科技大学装备综合保障技术重点实验室,湖南长沙410073

出　　处：《国防科技大学学报》2016年第3期141-147,共7页Journal of National University of Defense Technology

基　　金：国家自然科学基金资助项目(51375484;51205401;51475463);国防科学技术大学博士生跨学科联合培养计划资助项目(kxk140301)

摘　　要：将信号稀疏分解理论引入到轴承故障检测问题中,提出新的轴承故障检测方法。通过字典学习的方式可有效实现轴承正常状态振动信号稀疏表示的超完备字典。利用该字典只适用于轴承正常状态信号稀疏分解的特点,将待分析信号在该字典上展开,通过比较信号稀疏表示误差与所设定阈值的关系来判断轴承对应的状态,从而实现轴承的故障检测。实验结果表明:当误差阈值设置合理时,该方法可有效地判断出轴承是否发生故障。A new bearing fault detection method based on the signal sparse decomposition theory was developed. An over-complete dictionary on which the bearing vibration signals in normal state can be represented sparsely was trained by the dictionary learning method. According to the fact that this dictionary just can sparsely represent the signals in normal state,the bearing vibration signal in unknown state was decomposed on this dictionary. The bearing state was determined by comparing the representation error of the signal on the dictionary with the given error threshold,and then the bearing fault detection was achieved. Experimental tests validate the effectiveness of the proposed method in bearing fault detection when setting an appropriate error threshold.

关 键 词：轴承故障检测 稀疏分解 字典学习 稀疏表示误差 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]