A Wrapper Approach Based on Clustering for Sensors Selection of Industrial Monitoring Systems

Industrial processes are characterized to be in open environments, with high uncertainty, unpredictability and nonlinear behavior. They have to be monitored and measured rigorously due to their behavior having a direct and serious impact on product quality, safety, productivity, pollution and finance. However, industrial processes have enormous volumes of complex and high dimensional data available, with poorly defined domains and redundant, noisy or inaccurate measures with unknown parameters. Therefore, using just relevant and informative variables will decrease the high dimensionality and will facilitate the use of techniques to find patterns in data to correctly identify the functional states of the process, improving the performance of monitoring and measuring tasks. In this paper, we address the problem of sensor selection in industrial processes, where a mathematical or structural model and the class labels are not available or suitable. We propose a wrapper feature selection approach based on clustering, to perform an accurate process dataset classification with minimal variables needed. The proposed method is applied on an intensification reactor, the 'open plate reactor (OPR)', over thiosulfate and esterification reactions. Results are compared with previous work on the same datasets showing that fewer variables are needed to correctly identify all the functional states of the process.