Study to the Classification of Multi-Malfunction of Support Vector Machine
The paper studies the application of multi-value classification support vector machine (SVC) on the diagnosis to the mechanical malfunction, and makes classification experiment with the vibration single of rolling bearing. The experiment shows that SVM processes better classification performance and spreading potential than RBF manual neural network under the small study sample condition. Application of SVM offers the technical guarantee for the monitor and intelligent malfunction diagnosis to the computerized equipment.
In order to realize functions of teaching training and research for Steam Power Plants (SPP) simulator, the paper designed and implemented the Monitor & Control System (MCS) of the SPP simulator, and its implement based on a new type of software-GL Studio. The paper also introduced the virtual MCS technology based on GL Studio and the real-time communication between simulation models and the virtual MCS. Finally, the virtual MCS and simulation models of SPP were tested. The results showed that the virtual MCS based on GL Studio development was high fidelity and facilitate the upgrading, the virtual MCS can be used for teaching training and scientific research.