(64c) Big Data Approach to Fault Detection and Diagnosis in Batch Processes Using Nonlinear SVM-Based Feature Selection

Authors: 
Onel, M., Texas A&M Energy Institute, Texas A&M University
Kieslich, C. A., Texas A&M University
Guzman, Y. A., Princeton University
Floudas, C. A., Texas A&M University
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
Simultaneous achievement of high process efficiency, safety, and profitability is of utmost importance in modern process industries [1]. A major challenge in industrial applications is the rapid detection and identification of process faults in order to sustain a safe operation and minimize losses in productivity [2]. Statistical process monitoring (SPM) algorithms [3] are often used to detect the deviation from normal operating regimes. A growing number of studies have focused on data-driven process monitoring by using the Tennessee Eastman [4] and Pensim [5] benchmark datasets for continuous and batch processes respectively [6-9].

Batch reactor processes are widely used in chemicals, food, and pharmaceutical industry. These processes involve a considerable number of interconnected variables. In addition to inherent non-stationarity, batch processes are characterized with finite duration, nonlinear response, and batch-to-batch variability [10-12]. High complexity as well as dimensionality of batch processes impose a big challenge in fault diagnosis. Most novel techniques for fault detection and identification have focused on continuous processes, and the need of monitoring algorithm development for batch processes is evident [13].

We present a new data-driven framework for process monitoring and intervention in batch processes. Central to the framework are novel theoretical and algorithmic developments in support vector machine-based dimensionality reduction which improve accuracy, guide fault diagnosis, and encapsulate highly nonlinear relationships. We will discuss critical data processing and feature extraction steps specific to batch processing. Our methods will be applied to a recent extensive benchmark dataset [13] which features data describing 90,400 batches with numerous and diverse fault types. The analysis framework aims for early detection of faulty batches and enables intervention to reduce loss of profit.

References

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