Statistical pattern recognition and data clustering techniques in industrial plant

Abstract

In modern day collection of information from different sources, classification of information according to their property, traits etc and arranging the information according to the sub group is very important in nature. The whole task of collection, classification and clustering (arranging the sub group of data) is very challenging in nature. Different researchers are working to find a better way to classify data, recognize the data and cluster the data. The data collection, classification and clustering is of prime importance of different industry. newlineIn industrial plant, where there are thousands of sensors and transducer to provide online streaming of process variable data, data collection, data processing is a very challenging job. Many a times the data is crippled and distorted by different kind of noises. So different noise resistant classification, pattern recognition algorithms and clustering algorithms has to be used. The data processing done using these steps in the industry helps the management to see the overall performance of the plant and act on the drawbacks of the system to fine tune the system. In industrial plant, these steps are useful in statistical process control application. newlineThis research studies different aspect of pattern classification, pattern recognition and data clustering algorithms in general. A industrial process control application is taken in to consideration for implementation of different classification, recognition and clustering algorithms. Different noise resistant classification and recognition algorithms are also studied as these algorithm finds wide spread use in newlineviii newlinenoisy industrial environment. A PSO (Particle Swarm optimization method) based clustering algorithm is proposed in this thesis, which can cluster the subgroup of data in an efficient manner. newline