Zhang, Haigang; Zhang, Sen; Yin, Yixin Multi-class fault diagnosis of BF based on global optimization LS-SVM. (Chinese. English summary) Zbl 1424.94106 Chin. J. Eng. 39, No. 1, 39-47 (2017). Summary: Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems, a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem. Firstly, the variable metric discrete particle swarm optimization algorithm was employed to optimize the feature selection and LS-SVM parameters. Secondly, the feature vector was compressed by kernel principal component analysis. Finally, the heuristic error correcting output codes were constructed on the basis of Fisher linear discriminate rate. In the fault diagnosis scheme, fewer LS-SVM classifiers were applied through meaningful partitions and recombination of fault training samples. Simulation results show that the proposed fault diagnosis method can not only improve the fault detection accurate rate, but also enhance the timeliness of the entire system. MSC: 94C12 Fault detection; testing in circuits and networks Keywords:blast furnaces; fault diagnosis; least-squares analysis; support vector machines; global optimization PDFBibTeX XMLCite \textit{H. Zhang} et al., Chin. J. Eng. 39, No. 1, 39--47 (2017; Zbl 1424.94106)