基于小波包能量熵和DBN的轴承故障诊断

Bearing fault diagnosis based on wavelet packet energy entropy and DBN

轴承是旋转机械设备的关键部件,目前已有很多轴承故障诊断方法,但其中一些方法只能针对特定的轴承故障进行诊断,可能不适用于其他轴承故障问题,而且大部分方法的诊断准确率还可以进一步提高。提出小波包能量熵与深度置信网络(DBN)相结合的方法进行轴承故障诊断。首先对轴承振动信号进行小波包变换,然后以能量熵的形式构建特征向量,这些特征向量含有不同频段内的振动能量大小,可以用于区分各种轴承故障。最后利用基于DBN的深度模型对能量熵特征向量进行故障识别。使用两类轴承数据集进行验证,分别获得100%和99.5%的故障识别准确率。实验结果表明,该诊断方法具有较好的通用性,而且可以达到很高的诊断准确率。

Bearings are critical components of rotary machinery equipment.Numerous studies have been conducted on bearing fault diagnosis.Some of these methods can only be used for diagnosis of a certain type of bearing failure and cannot detect other failures.The diagnostic accuracy rate for most methods can be further improved.A new method is proposed for bearing fault diagnos is based on wavelet packet energy entropy and deep belief network(DBN).The bearing vibration signal is processed using wavelet packet transform to get the energy entropy feature vector.The feature vector represents the vibration energy in different frequency bands,which can be used to distinguish the fault type.The deep model based on DBN is adopted to recognize fault types.The proposed method achieves 100%and 99.5%fault recognition accuracy on two bearing datasets,respectively.The experimental results show that the proposed method has good versatility and can achieve high diagnostic accuracy.