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中文摘要：
针对在二维空间中，由变量均值偏移量较小但协方差不变所导致地故障类重叠，并因此降低故障识别率的问题，提出一种基于主成分分析（PCA）修整和线性判别分析（LDA）的新方法（PLDA）．该方法通过减弱不同故障类的重叠主成分对分类的影响，达到提升LDA故障识别率的目的．对24种具有代表性故障组合的模拟样本运用10次10倍交叉验证，试验结果表明PLDA算法的平均故障识别率为94．6％，远高于传统的核化LDA算法和LDA算法的60．0％和61．9％．
英文摘要：
In two space dimensions, faults tend to overlap each other as a result of a small shift of mean of variables with the same covariance. Moreover, overlapping could reduce the rate of identifying faults. To overcome this problem, a new algorithm, called PLDA, was proposed by integrating PCA-shaping and LDA. This algorithm declined the influences of overlapping principal-components of different fault classes on classification in order to increase the rate of LDA. The result of a validation of 24 kinds of representative simulation combinations by using 10-times and 10-fold cross validations reveals the average recognizing rate of fault is 94.6 %, compared to rates of 60.0% and 61.9% of KLDA and LDA respectively.
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