Fault Diagnosis of Lead-Zinc Smelting Furnace based on Multi-Class Support Vector Machines

Support vector machine (SVM) is powerful for the problem with small sampling, nonlinear and high dimension. A multi-class SVM classifier is applied to fault diagnosis of imperial smelting furnace in this paper. The input data is preprocessed through a special method based on the data reliability analysis technology, and six features are extracted as the input to multiple fault classifier for identify faults, which adapt an improved 'one to others' algorithm. The real application results show that the classifier has an excellent performance on training speed and reliability.