Multi-class fault diagnosis of BF based on global optimization LS-SVM
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摘要: 針對高爐故障診斷系統快速性和準確性的要求，提出基于全局優化最小二乘支持向量機的策略.首先，采用變尺度離散粒子群對最小二乘支持向量機的參數和故障特征的選取進行優化；然后，利用核主元分析法對選取的特征向量進行壓縮整理；最后，構造了以Fisher線性判別率為標準的啟發式糾錯輸出編碼.仿真結果表明，通過對故障訓練樣本有意義地分割重組，用較少的最小二乘支持向量機分類器，得到較高的故障判斷準確率且增強了整個系統的實時性.Abstract: 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.
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參考文獻
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