Data Fusion for Fault Diagnosis Using Dempster-Shafer Theory Based Multi-class SVMs

Abstract

AbstractThe multi-class probability SVM (MPSVM) is designed by training the sigmoid function to map the output of each binary class SVM into a posterior probability, and then combining these learned binary-class PSVMs using one-against-all strategy. The method of basic probability assignment is proposed according to the probabilistic output and performance of the PSVM. The outputs of all the binary-class PSVMs comprising an MPSVM are represented in the frame of Dempster-Shafer theory. A Dempster-Shafer theory based multi-class SVM (DSMSVM) is constructed by using the combination rule of evidences. To deal with the distributed multi-source multi-class problem, the DSMSVM is trained corresponding to each information source, and then the Dempster-Shafer theory is used to combine these learned DSMSVMs. Our proposed method is applied to fault diagnosis of a diesel engine. The experimental results show that the accuracy and robustness of fault diagnosis can be improved by using our proposed approach.KeywordsSupport Vector MachineDiesel EngineFault DiagnosisData FusionCentralize FusionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.