Classification of Unbalanced Fault Data Based on Similarity Factor Analysis of Sliding Window
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摘要: 针对机械装备监测过程中不均衡故障数据难以辨识,提出了一种基于滑动窗口相似性因子分析方法。该方法引入滑动窗口技术,通过分析目标数据与历史数据的PCA相似性因子,从旧的过程数据中筛选出与诊断目标相似的数据,构成待选数据池;然后采用距离相似性因子,从待选数据池中选择出与目标数据最相似的数据用于辅助训练。将该方法用于转子故障的不均衡数据分类中,在不同偏斜率下采用KPCA-SVM方法进行故障分类。结果表明:该方法可有效地改善分类决策边界,降低由样本不均衡而引起的误诊断率。Abstract: Aiming at difficult identification of unbalanced fault data for the mechanical equipment monitoring, anovel method based on similarity factor analysis of sliding mode control is proposed.In this method, the slidingmode control technology is introduced, and a dataset pool is constituted by analyzing the principal componentanalysis similarity factor between the target data and historical date, which is from the old monitoring dataset.Thenthe most similar data which will be used in training is obtained by analyzing the distance similarity factor of targetdata and the data from the dataset pool.The kernel principal component analysis-support vector machines method isapplied in the unbalanced data classification of fault rotor.The result shows that this method can improve theclassification decision boundary effectively, and reduce the false diagnosis caused by the unbalanced samples.
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Key words:
- data transfer /
- efficiency /
- eigenvalues and eigenfunctions /
- failure analysis
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