A Real-time Fault Monitoring and Diagnosis for Batch Process Based on Dynamic Principal Component Analysis
Batch process monitoring methods based on multivariate statistics are mainly multiway principal component analysis (PCA), its problems are that monitoring process needs predicted data, unequal length process must be aligned on data processing and small batches of data can not modeled and so on. Therefore, this article proposes dynamic PCA modeling methods for batch process based on dynamic characteristics of the batch. The method uses time-lagged technology to regroup for each batch data of the model after obtaining procedure dynamic lag time constant, then all batches combination data make a whole, based on which the PCA monitoring is established. This article gives fusion algorithm for delay data diagnosing information redundancy problems. Ultimately it realizes real- time online fault monitoring and diagnosis. The simulation result shows that the proposed method is effective.
Batch Process Dynamic PCA Monitoring and Fault Diagnosis Unequal Length
Jia Mingxing Qiao Shengyang Lan Qing
College of Information Science and Engineering, Northeastern University, Shen yang, Liao ning 110004
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
2951-2955
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）