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中文摘要：
为实现旋转机械故障诊断的自动化与高精度,提出基于正交邻域保持嵌入和连续隐Markov模型的模型诊断方法。将活动件故障振动信号进行经验模式分解并构造Shannon熵得到高维特征向量,利用正交邻域保持嵌入将高维特征向量约简为低维特征向量,并输入到各个状态连续隐Markov链进行旋转机械的故障模式识别。通过深沟球轴承故障诊断实例验证了该模型的有效性。
英文摘要：
To realize automation and high accuracy of rotating machinery fault diagnosis,a model diagnosis method for rotating machinery was proposed based on Orthogonal Neighborhood Preserving Embedding（ONPE）and Continuous Hidden Markov Model（CHMM）.Firstly,the fault vibration signals of moving parts were decomposed by EMD and Shannon entropy was constructed to obtain high-dimensional eigenvectors.Then,the high-dimensional eigenvectors were compressed and simplified as the low-dimensional eigenvectors by ONPE.Finally,the low-dimensional eigenvectors were put into CHMM for fault pattern recognition.Fault diagnosis example of deep groove ball bearings proved the effectiveness of this new method.
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