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Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method
IEEE Transactions on Control Systems Technology ( IF 4.8 ) Pub Date : 2020-08-27 , DOI: 10.1109/tcst.2020.3015514
Jingfei Zhang , Qinghua Zhang , Xiao He , Guoxi Sun , Donghua Zhou

Rotating machinery plays an important role in large-scale equipment. The fault diagnosis of rotating machinery is of great significance and can increase industrial safety. Up until now, most existing fault diagnosis techniques have been proposed under the condition that only a single fault will occur at the same time. However, in industrial applications, compound faults are more common to take place due to the tight coupling of different components. To diagnosis compound faults accurately is of great significance to the safe operation of industrial equipment. A fused imbalance learning method is proposed in this article exploiting the nonlinear-mapping ability of neural networks. The dimensionless parameterization combined with time–frequency transformation method is utilized to extract data features and construct different evidence sources. Basic probability assignment with nested structure is generated from a novel weighted extreme learning machine based on sensitivity analysis. Evidence combination is implemented to obtain a final inference about the compound-fault class. Experiments are conducted on a large rotating machinery fault diagnosis experimental platform. Both single faults and compound faults in bearings and wheel gears of the large rotating machinery are considered. Experimental results illustrate the effectiveness of the proposed method.

中文翻译:

旋转机械复合故障诊断:一种融合不平衡学习方法

旋转机械在大型设备中占有重要地位。旋转机械的故障诊断具有重要意义,可以提高工业安全。迄今为止,大多数现有的故障诊断技术都是在只有一个故障同时发生的情况下提出的。然而,在工业应用中,由于不同组件的紧密耦合,复合故障更常见。准确诊断复合故障对于工业设备的安全运行具有重要意义。本文利用神经网络的非线性映射能力,提出了一种融合不平衡学习方法。利用无量纲参数化结合时频变换方法提取数据特征,构建不同的证据源。具有嵌套结构的基本概率分配是由一种基于敏感性分析的新型加权极限学习机生成的。实施证据组合以获得关于复合故障类别的最终推断。实验在大型旋转机械故障诊断实验平台上进行。考虑了大型旋转机械轴承和齿轮的单一故障和复合故障。实验结果说明了所提出方法的有效性。考虑了大型旋转机械轴承和齿轮的单一故障和复合故障。实验结果说明了所提出方法的有效性。考虑了大型旋转机械轴承和齿轮的单一故障和复合故障。实验结果说明了所提出方法的有效性。
更新日期:2020-08-27
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