Fault Diagnosis Approach of Bayesian Networks Based on Multi-sensor Information Fusion and Application
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摘要: 针对齿轮泵信号具有复杂性和模糊性的特点,提出了一种基于多传感器信息融合的贝叶斯网络故障诊断方法。分析了齿轮泵振动和压力信号特点,以此为基础提取了振动信号的能量特征、分形特征和压力信号的高频压力脉动3种特征属性,构建了多故障贝叶斯网络对特征进行融合,设计了贝叶斯分类器,通过最大后验概率准则识别故障类型。两次融合结果表明:多传感器信息完备了特征空间,提高了诊断正确率,能够有效实现齿轮泵多种故障的诊断,具有较好的应用价值。Abstract: Aiming to the complex and fuzz nature of gear pump signal,a fault diagnosis approach of Bayesian net-works was presented based on multi-sensor information fusion. Firstly,this study analyzed the signal characteristicof vibration and pressure, and extracted energy and fractal characteristis of vibration signal and high frequency pres-sure pulse of pressure signal. Then the multi-fault Bayesian network was built to fuse these characteristics. Finally,Bayesian classifier was designed to identify the fault pattern through the maximum posterior estimation. Twice fusionresults show that multi-sensor information completes the characteristic space,raises the diagnosis exactitude rate.This approach can effectively carry out the gear pump multi-fault diagnosis and have better application value.
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Key words:
- multi-sensor information fusion /
- bayesian network /
- gear pump /
- fault diagnosis
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