(646b) Big Data Approach to Fault Detection, Diagnosis and Maintenance Optimization in Batch Processes
In this work, 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 nonlinear support vector machine-based feature selection [4, 5] which encapsulates highly nonlinear relationships between features, thus improving fault detection model accuracy and guiding fault diagnosis. We present results of a recent extensive benchmark, simulation dataset on Pensim model [6, 7] consisting 90,400 batches with numerous and diverse fault types, where we train time-specific models on the pre-aligned batch data trajectories in a moving horizon basis. The developed analysis framework aims for early detection of faulty batches and enables intervention to reduce loss of profit. Furthermore, simultaneous achievement of high process efficiency, safety and profitability is of utmost importance in modern process industries . Therefore by using the insights attained on fault detection latency and preventative action from the built models, we will extend the discussion to the prediction of batch process duration as well as maintenance optimization of batch process operations [9, 10].
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