国防科技大学学报2016，Vol.38Issue(3)：141-147,7.DOI:10.11887/j.cn.201603024
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
摘要
Abstract
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Key words
bearing fault detection/sparse decomposition/dictionary learning/sparse representation error分类
信息技术与安全科学引用本文复制引用
张新鹏,胡茑庆,程哲,胡雷,陈凌..信号稀疏分解理论在轴承故障检测中的应用[J].国防科技大学学报,2016,38(3):141-147,7.基金项目
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 （）
