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Optimal classifier design based on pairwise statistical separability maximisation of time-frequency features

机译：基于时频特征的成对统计可分离性最大化的最优分类器设计

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This paper presents a novel classification algorithm based on the time-frequency features extracted from multiple-sensor signals. Multiple-sensor signals are difficult to handle for classification purpose since each signal may have a different separability measure between classes and, hence, it may be difficult to pick a set of best sensors for classification. This paper provides a new separability measure, the so-called miss-classification probability, in order to overcome such a difficulty. A mathematical representation of the statistical aspect of the time-frequency features is introduced for efficient calculation of the miss-classification probability. Yet, another difficulty may be encountered in extracting a set of time-frequency features, which may best represent the difference among classes. This paper also proposes a pairwise statistical separability maximisation scheme to overcome this difficulty. The resultant classification algorithm based on these new developments is validated through seeded-fault tests with rotary compressors.
机译：本文提出了一种基于多传感器信号时频特征的分类算法。由于每个信号在类之间可能具有不同的可分离性度量，因此难以为分类目的处理多传感器信号，因此，可能很难选择一组最佳传感器进行分类。为了克服这种困难，本文提供了一种新的可分离性度量，即所谓的未分类概率。引入了时频特征的统计方面的数学表示，以有效地计算未分类概率。然而，在提取一组时频特征时可能会遇到另一个困难，这可能最能代表类别之间的差异。本文还提出了一种成对的统计可分离性最大化方案来克服这一困难。基于这些新进展的最终分类算法通过旋转压缩机的种子故障测试得到验证。

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