"Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation"

In this paper proposes different conventional and fuzzy based clustering techniques for fault detection and isolation in process plant monitoring. Process plant monitoring is very important aspect to improve productiveness and efficiency of the product and plant. This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data. This paper also discusses the comparison for K means algorithm and fuzzy C means algorithm.