Abstract
In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time series Kalman filter (TSKF) with the pruned exact linear time (PELT) approach to detect outliers is proposed. The proposed method outperformed benchmark methods in outlier removal performance using simulated data sets of dynamic systems with mean shifts. The method was also able to maintain the integrity of the original data set after performing outlier removal. In addition, the methodology was tested on industrial flaring data to pre-process the flare data for discriminant analysis. The industrial test case shows that performing outlier removal dramatically improves flare monitoring results through Partial Least Squares Discriminant Analysis (PLS-DA), which further confirms the importance of data cleaning in process data analytics.
Highlights
Modern process industries rely on dependable measurements from instrumentation in order to achieve efficient, reliable and safe operation
The application of the proposed pruned exact linear time (PELT)-time series Kalman filter (TSKF) method and Partial Least Squares Discriminant Analysis (PLS-DA) is tested with flare data sets taken from the petrochemical industry
In the flare monitoring case study, on the one hand, we demonstrate that applying data-driven approaches like partial least squares (PLS)-DA can effectively assist process engineers; on the other hand, the efficacy of new PELT-TSKF has been proved in ameliorating the quality of industrial flaring data which contain multiple operating points, and improving the resulting flare monitoring performance by increasing the anomaly detection rate and reducing possibilities of false alarms
Summary
Modern process industries rely on dependable measurements from instrumentation in order to achieve efficient, reliable and safe operation. To this end, the concept of the Internet of Things is receiving wider acceptance in the industry. The concept of the Internet of Things is receiving wider acceptance in the industry This is resulting in facilities with massively instrumented intelligent sensors, actuators, and other smart devices gathering real-time process knowledge at a high frequency. In order to take take full advantage of such abundant data streams, we need to extract useful information from them as outlined in the vision for future smart manufacturing platforms [1]. A typical application of processing large data streams is flare monitoring. We will demonstrate that plant-wide historical data can be used to monitor flares effectively
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