Fault Detection of Non-Linear Processes Using Adaptive Kernel Independent Component Analysis

• Main Authors: Hsueh-Che Kuo, 郭學哲
• Other Authors: Chun-chin ,Hsu
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/68381612942526303946

碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 99 === Kernel Independent Component Analysis is developed to deal with a non-linear dataset by Francis and Michael in 2002. In order to ensure the plant safety and produce high quality product, on-line process monitoring of a chemical process is an important issue. A chemical process usually behaves properties of non-Gaussian, non-linear and dynamics. Even though KICA can handle non-linear process, it fails to monitor dynamics process(i.e. autocorrelation). Besides, the traditional KICA used Mahalanobis distance to monitor the process. It implies only the recent observation is used, but previous observations are ignored. Thus, KICA may fail to monitor small process shifts. In order to overcome these drawbacks, this study will present an adaptive statistic to monitor a non-Gaussian, non-linear and dynamic process which is common encounted in a chemical process. At first, the collected data matrix is augmented by adding lagged variables. This preprocessing step is used to deal with the autocorrelation property of the chemical process. After that, the augmented matrix is then projected into a higher dimensional space (i.e. feature space) in order to take non-linear property into consideration. In the aftermath, the Exponentially Weighted Moving Average (EWMA) is used to extracted ICA components to store the information of past observations. Finally, the adaptive statistic is then developed by integrating EWMA and ICA extracted components. We will show the traditional ICA monitoring statistic is a special case of the proposed one. The efficiency of the proposed method will be verified by implementing two examples. For the first example, a simulation example is used for sensitivity analysis of the proposed statistic. After that, the Tennessee Eastman (TE) process is used for comparing traditional monitoring methods (i.e. ICA and KICA) and proposed method. Results indicate the proposed statistic is superior for monitoring small process shifts when compare with traditional methods.