Searching For Nuggets Of Gold In Your Process Data?

It is now common to have archival history of thousands of sensors sampled every second over long time. Yet we frequently have process engineers complain:??.We are drowning in data but starving for information??. How can these rich data sets be put to use? This paper addresses the issue of information and knowledge extraction from data with emphasis on process and performance monitoring including fault detection and isolation. Most of the major plant, factory, process, equipment and tool disruptions are avoidable, and yet preventable fault detection and diagnosis strategies are not the norm in most industries. It is not uncommon to see simple and preventable faults disrupt the operation of an entire integrated manufacturing facility. For example, faults such as malfunctioning sensors or actuators, inoperative alarm systems, poor controller tuning or configuration can render the most sophisticated control systems useless. Such disruptions can cost in the excess of $1 million per day and on the average they rob the plant of 7% of its annual capacity. Over the last decade the fields of multivariate statistics and data visualization and analysis methods have merged to develop powerful sensing and condition-based monitoring systems for predictive fault detection and diagnosis.These methods rely on the notion of sensor fusion whereby data from many sensors or units are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. Such methods are at a stage where these strategies are being implemented for off-line and on-line deployment. This article attempts to outline the field of sensor fusion - the application of signal processing methods, in the temporal as well as spectral domains, on a multitude and NOT singular sensor signals to detect incipient process abnormality before a catastrophic breakdown is likely to occur Keywords: Process analytics, process and performance monitoring, multivariate statistical analysis, sensor-fusion