Abstract:
Quality monitoring has been a new research hot topic in recent years. The alarm should be issued only when the product quality is outside the normal range. However, the traditional neighborhood preserving embedding (NPE) may issue alarm to all faults, which inevitably results in lots of downtime and seriously affect the normal production. Aiming at the above problems, this paper proposes a total-time series neighborhood preserving regression (T-TSNPR) modeling approach. Firstly, by considering the influence of unrelated variables on feature extraction, the correlation analysis between process variables and quality variables is performed, and the contribution method is used to select the key variables. Secondly, in order to achieve the dimensionality reduction in dynamic process, the neighborhoods within certain timeframe are selected to construct the localized constraining relationship between neighborhoods such that the quality-related information can be extracted through total projection regression. Thirdly, Hotelling’s
T2 statistic is established for online quality monitoring. Finally, a numerical example and the Tennessee-Eastman process are provided to verify the effectiveness of the T-TSNPR algorithm.