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.
Highlights
The advent of data historian systems has turned the chemical industry into a prime generator and depository of large-scale datasets, typically in a time series format
We provide an overview of recently-developed visualization techniques for process data
The concept underpinning these techniques is a time-explicit Kiviat diagram, which allows for plotting multivariate time series data collected during the operation of chemical processes
Summary
The advent of data historian systems has turned the chemical industry into a prime generator and depository of large-scale datasets, typically in a time series format. Data historians collect and store measurements from potentially hundreds or thousands of sensors and actuators, often with sub-minute frequency. In many cases, these “big data” sets cover several years or even decades, and their sheer volume is often mentioned as a major obstacle towards extracting the valuable and actionable information contained therein. Process operators frequently find themselves “drowning in data” [1], citing, amongst others, the lack of time and human resources required to analyze (“mine”) these data, as well as the lack of appropriate tools, as a significant impediment. In the context of this paper, we will focus on the latter and refer the reader to the thorough review by Venkatasubramanian et al [2] for more information on model-based methods
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