Study of Data Clustering Techniques Based on Various Artificial Neural Network and Particle Swarm Optimization: Improving Fault Detection and Isolation in Process Plant Monitoring

Abstract

This paper proposes different conventionaland fuzzy based clustering techniques for fault detection and isolation inprocess plant monitoring. Process plant monitoring is very important aspect toimprove productiveness and efficiency of the product and plant. This papertakes a case study of plant data and implements SOM based training methods tocluster the data and detect and isolate the faults. It also discusses a newhybrid clustering algorithm implementing fuzzy C means algorithm and PSO.