A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.
A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.
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DOI：
10.1371/journal.pone.0125703
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Huang Q
中科院分区：
文献类型：
--
作者：
Liu Z;Liu Y;Shan H;Cai B;Huang Q
提出了一种基于集成经验模式分解（EEMD）和贝叶斯网络的齿轮泵故障诊断方法。本质上，该方案是一个多源信息融合的方法。与传统的EEMD故障诊断方法相比，该方法能够充分利用传感器信号以外的所有有用信息。该诊断贝叶斯网络由故障层、故障特征层和多源信息层组成。采用EEMD方法对传感器测量的振动信号进行分解，计算固有模态函数（IMF）的能量作为故障特征。这些特征被添加到贝叶斯网络的故障特征层中。其他有用信息源被添加到信息层。广义三层贝叶斯网络可以充分结合故障和故障症状以及其他有用的信息，如肉眼检查和维修记录。因此，可以提高诊断准确性和能力。将该方法应用于齿轮泵的故障诊断，建立了贝叶斯网络的结构和参数。与人工神经网络和支持向量机分类算法相比，该模型在仅使用传感器数据时具有最佳的诊断性能。实例研究表明，从人的观察或系统的维修记录的一些信息是很有帮助的故障诊断。它是有效的和高效的故障诊断基于不确定性，不完全信息。
This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.
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影响因子：
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