This paper presents the results of fault detection in a reaction system for the production of cyclopentenol in a CSTR (Continuous Stirred Tank Reactor) with three simulated faults, utilizing the techniques of statistical machine learning support vector machine SVC and SVR, and for a jacketed CSTR with one simulated fault, the dimensionality reduction technique DPCA (Dynamic Principal Component Analysis) is also compared with the evaluated SVM techniques.
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The SVM technique has been used for various applications such as face recognition, time series forecasting, fault detection and modeling of nonlinear dynamical systems. Thus, this methodology can provide a single solution with a strong regularized feature that is very suitable for classification problems poorly conditioned. The support vectors utilize a hyperplane with maximum margin to separate different classes of data producing a satisfactory overall performance. Vapnik, and provides a powerful tool for pattern recognition to deal with problems that have nonlinear, large and limited data sample. The original SVM algorithm was proposed by Vladimir N. Nowadays, the Support Vector Machine (SVM, also Support Vector Networks) is an alternative for fault detection and diagnostics. There are different techniques for fault detection in the literature. An important source of control degradation and safety issues are caused by faults in process control loops. There are processes variations that might be connected to various sources, so, process plants containing control loops with poor performance are often found in an industrial scenario. Discovering abnormalities in control systems is a very important task.
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It also contains action such as diagnosing possible causes of problems that may degrade the productive capacity of the process, alarms management and providing strategies on how to act to maintain or even improve the operation efficiency. Introduction The monitoring of control systems is related to the ability of supervising the operation of industrial plants while evaluating the loss of performance caused by oscillations, disturbances, faults in sensors, and valve stiction.
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