New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.
New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.
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基于信息熵和概率神经网络的旋转机械故障识别新方法
DOI：
10.3390/s18020337
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发表时间：
2018-01-24
期刊：
影响因子：
--
通讯作者：
Xu F
中科院分区：
文献类型：
--
作者：
Jiang Q;Shen Y;Li H;Xu F
特征识别和故障诊断对设备安全和旋转机械的稳定运行起着重要作用。针对旋转机械振动信号的复杂性问题，提出了一种基于信息熵和概率神经网络的特征融合模型。该方法首先利用信息熵理论提取振动信号的三种特征熵，即奇异谱、功率谱熵和近似熵。然后构建特征融合模型对故障信号进行分类诊断。该方法能够综合不同方面的信息，对故障特征更加敏感。对模拟故障信号的实验结果验证了该方法较好的性能。在实际的两跨转子数据中，新方法的故障检测准确率比分别使用三种信息熵的方法提高了10%以上。实践证明，该方法是一种有效的旋转机械故障识别方法。
Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.
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