基于无监督学习的时序序列故障诊断方法研究

Research on Time Series Fault Diagnosis Method Based onUnsupervised Learning

随着信息技术和传感器技术的发展,基于数据驱动的故障诊断技术是保障大型工业设备高效、安全运行的关键技术之一.机器学习由于其具有强大的特征表示能力与基于大数据的特征提取优势,多种深度神经网络模型已成为故障诊断领域中最常用的特征提取方法之一.但监测设备收集到的数据中包括大量的无标签数据,基于监督学习的深度神经网络模型没有办法对其进行充分的利用,造成了部分有用信息的浪费.针对无标签数据,提出基于无监督学习的方法,利用最大化互信息的思想训练特征提取模型,在此基础上,设计一种针对时序序列数据的故障诊断方法,并在公开数据集凯斯西储大学轴承数据集上验证,取得了比以往传统方法更高的诊断精度.在卫星监测数据上进一步验证,提出的特征提取模型能够对故障不同阶段进行区分,很好地捕捉不同阶段的数据特性.结果表明,提出的基于无监督学习的故障诊断方法能够有效、充分地利用大量的无标签数据,提高时序序列数据的故障诊断精度.

With the development of information technology and sensor technology,data-driven fault diagnosis technology is one of the key technologies to ensure the efficient and safe operation of large industrial equipment.Due to its powerful feature representation ability and the advantages of feature extraction based on big data,machine learning has become one of the most commonly used feature extraction methods in the field of fault diagnosis.However,the data collected by the monitoring equipment includes a large amount of unlabeled data,and the traditional deep neural network model does not make full use of it,resulting in the waste of some useful information.For unlabeled data,we adopt the idea of unsupervised learning,train a feature extraction model by maximizing mutual information,and on this basis,we design a fault diagnosis method for time series data,and verify it on the public dataset Case Western Reserve University bearing dataset,achieving higher diagnostic accuracy than previous traditional methods.Further verification on satellite monitoring data,our feature extraction model can distinguish different stages of failure and capture the data characteristics of different stages.The results show that the fault diagnosis method based on unsupervised learning proposed in this paper can effectively and fully utilize a large amount of unlabeled data and improve the fault diagnosis accuracy of time series data.