摘要
传统基于主元分析的故障检测方法大多假设工业过程只运行在1个稳定工况,数据服从单一的高斯分布。若这些方法直接用于多工况过程则将会产生大量的误检。为此,本文提出了1种基于高斯混合模型的多工况过程监测方法。首先利用PCA变换对过程数据集进行降维,在主元空间建立高斯混合模型对过程数据进行聚类,自动获取工况数和相关分布特性。然后对每个工况建立主元分析(principal component analysis,PCA)模型来描述整个运行过程数据分布的统计特性。最后在过程监测中,根据监测样本属于各个工况的概率构造综合统计量,实现对多工况过程的故障检测。TE过程的仿真结果表明,本文提出的方法与传统的PCA方法相比,能自动获取工况和精确估计各个工况的统计特性,从而能更准确及时地检测出多工况过程的各种故障。
Traditional fault detection methods based on pinciple component analysis（PCA） rely on the assumption that the process has one nominal operating region and the operating data follow a unimodal Gaussian distribution.The application of these approaches to an industrial process with multiple operating modes would always trigger false alarms.Thus,a new multimode process monitoring approach based on Gaussian mixture models（GMM） is proposed in this paper.First a GMM is constructed in the model subspace obtained by PCA transformation to characterize the multiple operating regions,each of which corresponds to a Gaussian component.Then a principal component model is established for individual operating mode to describle the statistical features of whole operating process.Finally,an overall statistics chart,according to the posterior probability of a monitored sample belong to each Gaussian component,is defined to monitoring the multimode process.The validity and effectiveness of the proposed monitoring approach is illustrated by the Tennessee-Eastman challenge process.The comparison of monitoring results demonstrated that the proposed approach can achive a good parameters estimation of the multiple operating regions with automatically select the number of modes.Therefore,it is superior to the conventional PCA method and can achieve accurate and early detection of various types of faults in mulitimode processes.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2010年第1期17-22,共6页
Computers and Applied Chemistry
基金
国家创新研究群体科学基金项目(60721062)
国家高技术研究发展计划项目(2007AA04Z162)
关键词
多工况
高斯混合模型
故障检测
统计监控
multiple mode, gaussian mixture models, fault detection, statistical monitoring
