基于改进后半监督深度信念网络的多工况轴承故障诊断研究

Research on Multi-condition Bearing Fault Diagnosis Based on Improved Semi-supervised Deep Belief Network

随着工业自动化的发展,各类旋转机械在多工序、多任务约束下,其轴承转速与载荷均会发生变化,导致针对单一工况的故障诊断方法的准确率大打折扣。因此,提出一种多工况约束下轴承故障诊断方法,该方法基于半监督深度信念网络(Semi-supervised deep belief network,SSDBN),利用少量标记数据即可完成故障分类、判断,提高了跨工况故障诊断的准确率。首先将源域与目标域设置为负载相同、转速与损伤尺寸不同的数据集,通过小波包分解对源域和目标域信号进行重构;在此基础上,应用最大平均差算法(Maximum mean discrepancy,MMD)作为源域和目标域数据特征分布差异评价指标,筛选出分布差异较小的特征样本数据;利用改进后的半监督深度信念网络训练较少的标签数据和大量无标签数据,提高待测数据的分类精度;最后,以凯斯西储大学轴承数据集为例进行试验,验证相同负载不同转速与损伤尺寸工况下模型的诊断精度,以及不同负载、转速、损伤尺寸工况下的模型诊断精度。结果表明该方法能够提高轴承在多工况约束下的故障诊断准确率,减少故障诊断的误报率,并能降低训练模型过程中的梯度消失现象,提高故障分类成功的概率。另外,提出的方法无需考虑各种特征的灵敏度,亦无须依赖专家知识与经验,具有较强的普适性与兼容性,有利于及时发现并替换损坏轴承,保证机械设备安全、可靠地运行。

With the development of industrial automation,the processes in the production of rotating systems are becoming more and more sophisticated and complex.When multiple processes are executed,the internal bearing speed and load are constantly changing.For this type of multi-working conditions,the original fault diagnosis method trained in a single working condition is no longer applicable.Therefore,a bearing fault diagnosis method for bearings under multiple operating conditions is proposed.This method is based on a semi-supervised deep belief network(Semi-supervised deep belief network,SSDBN).Using a small amount of labelled data to conduct the fault classification and judgment,and it can improve the accuracy of fault diagnosis under varying working conditions.First,set the source and target domains to the same load,different speed and damage size data set,and process the source and target domain signals by wavelet packet decomposition.Then the maximum mean discrepancy algorithm((Maximum mean discrepancy,MMD)is used as the evaluation indicator of the difference between the distribution of data features in the source and target domains,and the smaller distribution difference is selected.Feature sample data,make the model easier to compare features,improve the classification accuracy of the target domain data.Use the improved semi-supervised deep belief network to train less labelled data and a large number of unlabelled data,improve the classification accuracy of the data to be tested.Take the bearing data set of Western Reserve University as an example to verify the diagnostic accuracy of the model under the same load at different speeds and damage size conditions,as well as the diagnostic accuracy of the model under different load,speed and damage size conditions.The results show that the method can improve the accuracy of the fault diagnosis of the bearing under the constraints of multiple operating conditions,reduce the false alarm rate of the fault diagnosis,and reduce the gradient disappearance during the training model,and increase the probability of successful failure classification.In addition,the proposed method does not need to be considered the sensitivity of various features does not need to rely on expert knowledge and experience.It has strong universality and compatibility,which is helpful to find and replace damaged bearings in time and ensure the safe and reliable operation of machinery and equipment.