Probabilistic Fault Prediction of Incipient Fault

摘要：

In this work, a probabilistic fault prediction approach is presented for prediction of incipient fault in an uncertain way. The approach has two stages. In the first stage, normal data is analyzed by principle component analysis (PCA) to get control limits of the statistics of T 2 and SPE . In the second stage, fault data starts by PCA so as to derive the statistics of T 2 and SPE . Then, the samplings of these two statistics obeying some certain prediction distribution are obtained using Bayesian AR model on the basis of the Winbugs software. At last, one-step prediction fault probabilities are estimated by kernel density estimation method according to the statistics’ corresponding control limits. The prediction performance of this approach is illustrated using the data from the simulator of the Tennessee Eastman process.

关键词： principle component analysis (PCA) incipient fault probability fault prediction Bayesian auto-regression (AR)

作者: Zhen Zhao Fuli Wang Mingxing Jia Shu Wang

作者单位: School of Information Science and Engineering, Northeastern University, Shenyang, 110004 School of Information Science and Engineering, Northeastern University, Shenyang, 110004 Key Laborat

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3911-3915

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）