Reasoning under uncertainty: A data and model-based methodology for process monitoring

点击翻译按钮获取中文摘要

It is now common to have archival history of thousands of sensors sampled every second over long time periods. 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 research proposal is concerned with the issue of information and knowledge extraction from data with emphasis on safe process monitoring. 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 unplanned 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. Novelty : Whereas statistical inferencing has been the main engine in search tools, these same methodologies are not being used as effectively in the process industry. The main goal of this proposal is to develop a theoretical framework for obtaining models from archived process data using statistical inference schemes and subsequently use such models to make decisions under uncertain conditions. The specific objective is to develop methodologies on how to deal with a specific fault and develop hazard operability guidelines from the process connectivity information that can be captured from process data and validated with information from the piping and instrument diagrams. Significance: The socio-economic benefits of this proposal would be a safe, highly productive and energy efficient process monitoring methodology for processes that are otherwise difficult to model and operate.