Process fault detection using time‐explicit Kiviat diagrams

Significant amounts of data are collected and stored during chemical process operations. The corresponding datasets are typically difficult to represent and analyze using traditional visualization methods. This article introduces time‐explicit Kiviat diagrams as a means to visualize the multidimensional time series data acquired from plant operations. This framework is then used to build multivariate control charts for large scale time series datasets, and to develop a fault detection mechanism that lends itself to real‐time implementation. The proposed methodology is applied to an industrial case study as well as to data obtained from the Tennessee Eastman process simulator, showing very good performance. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4277–4293, 2015     

An improved methodology for outlier detection in dynamic datasets

A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier and innovational outlier detection problems in dynamic process dataset. Compared with current outlier detection methods, the new method enjoys the following advantages: (a) no prior knowledge of the process model is needed; (b) it is easy to tune; (c) it can be applied to both univariate and multivariate outlier detection; (d) it is applicable to both on‐line and off‐line operation; (e) it cleans outliers while maintains the integrity of the original dataset. © 2014 American Institute of Chemical Engineers AIChE J, 61: 419–433, 2015