Abstract
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 difficult to accurately identify because of their complexity and subjectivity. Aiming at this problem, the sparse representation theory is used to the field 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 coefficients, 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 coefficients of each fault signal on the comprehensive double sparse dictionary. Finally, the coefficient is used as the input of DBN to 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.
Highlights
Rolling bearing is a key component which is widely used in rotating machinery, the overall performance of the whole machine is often directly affected by its running state
In order to solve the problems of intelligent fault diagnosis, such as the traditional feature extraction process depends on prior knowledge and expert diagnosis experience, the low accuracy of fault diagnosis and the high time-consuming of diagnosis process when large data is processed, a novel fault diagnosis method based on double sparse dictionary learning joint with Deep Belief Network (DBN) is proposed in this paper
The double sparse sub-dictionaries of all types of fault signals are obtained based on double sparse dictionary learning model
Summary
Rolling bearing is a key component which is widely used in rotating machinery, the overall performance of the whole machine is often directly affected by its running state. Jing et al [25] developed a CNN to learn features directly from frequency data of vibration signals and tested the different performance of feature learning from raw data, frequency spectrum and combined time-frequency data These methods have achieved better classification results, there are still some problems, such as complex model, long training time and low diagnosis accuracy, etc. Another type of dictionaries obtained from the training of a set of sample signals themselves is produced by Machine Learning algorithms, their atoms have a good self-adaptability to sample signals, but due to their unstructured form, it is relatively difficult to solve this problem These dictionaries mainly include principal component analysis (PCA), method of optimal directions (MOD) and K-singular value decomposition (K-SVD) [29]. This paper combines the double sparse dictionary model and DBN to achieve rolling bearing fault diagnosis in the sparse domain. The vibration signals are sparsely coded in the double sparse dictionary, and the DBN performs intelligent fault diagnosis
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