基于加权JS散度分块策略的设备故障检测  

Fault Detection of Equipment Based on Weighted JS Divergence Partitioning Strategy

作　　者：孙楚栋 王业[1] SUN Chudong;WANG Ye(College of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830000,China)

机构地区：[1]新疆农业大学,计算机与信息工程学院,新疆乌鲁木齐830000

出　　处：《微型电脑应用》2024年第9期182-185,193,共5页Microcomputer Applications

摘　　要：在分块主成分分析(PCA)故障监测方法中存在2个主要问题,一是确定最佳的变量块划分阈值往往十分困难,二是PCA故障监测主要针对是服从正态分布的数据,而实际工业生产数据往往无法完全符合正态分布的要求。为了解决这些问题,提出一种基于加权JS散度分块策略的故障检测方法。通过观察变量的分布模式来判断过程变量的正态性,并将数据分为正态变量和非正态变量。为了解决变量块难划分问题,采用加权JS散度分块策略,将变量拓展为正态加权子块和非正态加权子块,再建立PCA和独立成分分析(ICA)检测模型对正态子块和非正态子块进行监控,在得到各个子块的检测结果后,采用贝叶斯融合推断方法将监测结果进行融合,得出全局的故障监测结果。通过在某卷烟厂制丝设备的故障数据上的应用验证了该方法的有效性和可行性。There are two main problems in the partitioning principal component analysis(PCA)fault monitoring method.One is that it is often difficult to determine the optimal variable block partition threshold.The other is that PCA fault monitoring is mainly aimed at data subject to normal distribution,while the actual industrial data often cannot fully meet the requirements of normal distribution.To address these issues,a fault detection method based on weighted JS divergence partitioning strategy is proposed.The normality of process variables is determined by observing their distribution patterns,and the data are divided into normal and non-normal variables.To solve the problem of difficult partition of variable blocks,a weighted JS divergence partitioning strategy is adopted,which expands variables into normal weighted and non-normal weighted blocks.PCA and independent compent analysis(ICA)detection models are then established to monitor normal and non-normal blocks.After obtaining the detection results of each block,Bayesian fusion inference method is used to fuse monitoring results to obtain the global fault monitoring results.The effectiveness and feasibility of this method are verified through the application of fault data on the silk production equipment of a certain cigarette factory.

关 键 词：故障检测 分块策略 加权JS散度 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]