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

The 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.

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