Data-driven discriminative K-SVD for bearing fault diagnosis

Rolling element bearing is an important component. As it is usually used in a complex environment, there are many failures occur on them. How to find the fault has become a pressing problem to be solved. The vibration signals generated by bearings are usually containing a variety of noise. The general diagnosis is divided into two stages: feature extraction and classification. Unlike conventional methods, there is no need to have a specific fault feature extraction step for sparse representation method. One of the dictionary learning methods which called the K-SVD is an algorithm does not need a defined dictionary but whose output is an over-complete dictionary studied by signals. The method that iteratively updating the K-SVD-trained dictionary based on the outcome of a linear classifier usually leads to the local minima. In order to train a dictionary that works well both in representation and classification, we use the Discriminative K-SVD which the labels are directly embedded in the dictionary learning step. Discriminant K-SVD can find all parameters of global optimum at the same time. The complexity of Discriminative K-SVD is related to that of K-SVD. Finally, the proposed method is applied in the practical bearing experiments, the results not only confirmed the accuracy of the proposed method for finding the fault types of bearings but also identified the damage degrees of bearings.