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中文摘要：
针对传统旋转机械单通道故障诊断的信息不完整以及缺少故障样本等问题，提出了基于全信息小波包和支持向量机的旋转机械故障诊断方法。运用小波包频道能量分解技术提取了全信息能量特征向量，以此作为支持向量机多故障分类器的故障样本，经训练的分类器作为故障智能分类器可对设备工作状态进行自动识别和诊断。实验研究表明：基于全信息小波包和支持向量机的故障诊断方法能准确、有效地对旋转机械的工作状态和故障类型进行分类，显著提高了故障诊断的准确率。
英文摘要：
Due to the insufficiency of traditional rotary machinery fault diagnosis with single channel signal and shortage of fault data samples, a rotary machinery fault diagnosis method base on full information wavelet packet and support vector ma- chine is proposed. Extracting the full information characteristic vector with the technology of wavelet packet frequency segment power decomposition and taking it as input fault of support vector machine multi - fault classifier, the trained classifier, as fault intelligent classification, had very strong identification capability, which could identify automatically the working state of rotary machinery. The experiment result shows that the proposed approach can classify working condition of rotary machinery accurately and effectively, and can improve fault diagnosis accuracy obviously.
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