Data Visualization and Visualization-Based Fault Detection for Chemical Processes

Abstract

Over the years, there has been a consistent increase in the amount of data collected by systems and processes in many different industries and fields. Simultaneously, there is a growing push towards revealing and exploiting of the information contained therein. The chemical processes industry is one such field, with high volume and high-dimensional time series data. In this paper, we present a unified overview of the application of recently-developed data visualization concepts to fault detection in the chemical industry. We consider three common types of processes and compare visualization-based fault detection performance to methods used currently.

Distance Matrix Patterns for Visual and Interpretable Process Data Analytics

“…Gajjar and Palazoglu used parallel coordinate visualization to develop a fault detection and diagnosis methodology that integrates PCA and random forest techniques. Wang et al − suggested using Kiviat diagrams, commonly known as radar charts, for visualization-based fault detection.…”