Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing

作者：Tong, Qingbin^*; Sun, Zhanlong; Nie, Zhengwei; Lin, Yuyi; Cao, Junci

来源：Journal of Vibroengineering, 2016, 18(8): 5204-5216.

DOI：10.21595/jve.2016.17566

• sparse decomposition
• alternating direction method of multipliers (ADMM)
• dictionary learning
• orthogonal matching pursuit (OMP)
• K-SVD
• rolling element bearing
• fault feature extraction

Sparse decomposition is a novel method for the fault diagnosis of rolling element bearing, whether the construction of dictionary model is good or not will directly affect the results of sparse decomposition. In order to effectively extract the fault characteristics of rolling element bearing, a sparse decomposition method based on the over-complete dictionary learning of alternating direction method of multipliers (ADMM) is presented in this paper. In the process of dictionary learning, ADMM is used to update the atoms of the dictionary. Compared with the K-SVD dictionary learning and non-learning dictionary method, the learned ADMM dictionary has a better structure and faster speed in the sparse decomposition. The ADMM dictionary learning method combined with the orthogonal matching pursuit (OMP) is used to implement the sparse decomposition of the vibration signal. The envelope spectrum technique is used to analyze the results of the sparse decomposition for the fault feature extraction of the rolling element bearing. The experimental results show that the ADMM dictionary learning method can updates the dictionary atoms to better fit the original signal data than K-SVD dictionary learning, the high frequency noise in the vibration signal of the rolling bearing can be effectively suppressed, and the fault characteristic frequency can be highlighted, which is very favorable for the fault diagnosis of the rolling element bearing.

• 出版日期2016-12
• 单位北京交通大学