Monitoring system for anomaly detection in plants with continuous operation

Abstract

Detection of anomaly in the operation of continuous plants and systems based on real time observations is a very important activity that aims at determining the health status of the system. This paper presents a methodology and algorithm for creating a computer monitoring system for anomaly detection in online mode. The proposed method includes creating Data Cloud models based on predefined equal size portions of data samplings and estimating the Similarity Level between them as a bounded value in the interval [0, 1]. Data cloud models use the concept of mesh of grid cells that capture the local density of the data points around the cells. The Similarity analysis is based on estimating the difference between the local densities in the two data clouds. The whole methodology is explained and illustrated in details in the paper by using numerous examples with real data. The proposed monitoring system is based on using the moving window concept for continuous analysis of the similarity level in online mode. The final section of the paper shows experimental results for anomaly detection based on using real operation data (temperatures) from a Petrochemical plant.