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中文摘要：
提出了一种基于双树复小波（DTCWT）和深度信念网络（DBN）的轴承故障诊断新方法。采用DTCWT对轴承振动信号进行分解实验，结果表明DTCWT能够很好地将信号分解到不同频带。进而提取能量熵作为故障特征，采用DBN小样本分类模型对轴承故障进行分类，并与传统分类器进行比较，结果表明该方法能准确识别不同故障类型，扩展了DBN在机械故障诊断中的应用。
英文摘要：
Based on DTCWT and DBN, a new method of bearing fault diagnosis was proposed. Ex- periments on bearing vibration signals decomposition show that the signals may be well decomposed into different frequency bands by DTCWT. Then, power entropy of different frequency bands were taken as the fault features and input to the model for classification and the traditional classifiers were taken as the comparison. Results show that the method may identify different fault types accurately, which expands the applications of DBN.
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