Fault diagnosis and knowledge management of turbo-generator based on support vector machine

Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. In the study, the proposed SVM model is applied to fault diagnosis of turbo-generator, and the method of knowledge management in SVM diagnostic system of turbo-generator is presented. The real data sets are used to investigate its feasibility in fault diagnosis of turbo-generator. The experimental results show that SVM not only has high diagnostic accuracy, but also has excellent anti-noise capability.