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中文摘要：
提出一种基于Hilbert谱图特征的转子故障智能诊断方法。首先,通过希尔伯特-黄变换（HHT）得到反映故障信号特征的Hilbert谱;然后,利用主成分分析（PCA）对故障信号的Hilbert谱进行特征提取;最后,对得到的特征数据使用野点检测进行分类,并用粒子群优化算法,自适应获得野点检测最优参数,实现转子故障的智能诊断。使用ZT-3型转子故障试验台实验数据对此方法进行了验证,并与传统频谱特征分类结果进行比较,结果表明了此方法的正确性。
英文摘要：
A method for intelligent diagnosis of rotor fault based on the characteristics of Hilbert spectrum is proposed.First,Hilbert spectrums with fault characteristics are obtained through Hilbert-Huang transformation.Then,characteristics of Hilbert spectrum are extracted by principal component analysis（PCA）.Finally,characteristics are classified using novelty detection,and parameters are optimized by particle swarm optimization,thus intelligent diagnosis is realized.In addition,the data from ZT-3 multiple-function experimental instrument are compared with those by the method of traditional frequency spectrum.The comparison confirmed the validity of our method.
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