Fault Diagnosis Using a Joint Model Based on Sparse Representation and SVM

In this paper, a novel fault diagnosis approach based on sparse representation and support vector machine (SVM) is proposed. By keeping excluding subdictionaries and representing the testing sample using the sparse representation model, the confidence of each class is computed through a Hough voting progress using the computed sparse coding to show how much chance the testing sample belongs to a certain class. And then, the maximal confidence is selected as the highest confidence of every class. If the maximal confidence is high enough, the corresponding class label is taken as the diagnosis outcome. Otherwise, an SVM process is applied to classes with higher confidence factors to give the final outcome. An integrated framework from feature selection to fault type classification is designed and used in the roller bearing fault diagnosis. The result shows that the accuracy of our approach can reach 91.7%, 2% higher than SVM approach, and 7% higher than the conventional sparse-representation-based classification. Finally, an experiment based on the sparse representation model alone was undertaken. The result shows that using a confidence threshold, this model can give a compromised diagnosis between diagnosis accuracy and the number of samples received to diagnosis, which is suitable in areas where high diagnosis accuracy is required such as nuclear and aerospace industries.