Feature extraction is the key technology in the data-driven intelligent fault diagnosis methods of rolling bearing. However, the acquired features by the traditional methods, which mainly based on time-frequency domain, sometimes cannot well represent the characteristics of the signal and are dif cult to accurately identify because of their complexity and subjectivity. Aiming at this problem, the sparse representation theory is used to the eld of fault diagnosis because the different types of rolling bearing
fault signals only has the highest matching degree with the dictionary atoms trained by the same type of fault signals. A novel method based on the double sparse dictionary model joint with Deep Belief Network (DBN)
is proposed for the fault diagnosis of rolling bearing. Firstly, each type of fault signal is trained according to the double sparse dictionary learning algorithm and the corresponding double sparse subdictionaries is
obtained. In order to reduce the feature dimension of sparse representation coef cients, the low contribution atoms of all subdictionaries are removed and the rest are recombined into a comprehensive double sparse dictionary. Then, the Orthogonal Matching Pursuit (OMP) algorithm is adopted to obtain the corresponding
sparse feature coef cients of each fault signal on the comprehensive double sparse dictionary. Finally, the coef cient is used as the input ofDBNto train and judge the faults of the rolling bearing. The experimental results show that the proposed method has higher diagnosis accuracy and stability compared with the traditional intelligent fault diagnosis methods, and the training and testing time of DBN is greatly reduced.