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

不确定性下的推理：基于数据和模型的过程监控方法

• 批准号: 3522-2012
• 负责人: Shah, SirishLalji
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568305
• 关键词: Reasoning; under; uncertainty; data; model

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.

现在，在很长一段时间内，每秒都会对数千个传感器进行采样，这是很常见的。然而，我们经常有流程工程师抱怨：“……我们淹没在数据中，却渴望获得信息……”。 如何将这些丰富的数据集投入使用？这项研究建议关注从数据中提取信息和知识的问题，重点是安全过程监控。 大多数主要的工厂、工厂、工艺、设备和工具中断都是可以避免的，但可预防的故障检测和诊断策略在大多数行业中并不是标准。简单而可预防的故障扰乱整个集成制造设施的运行并不少见。例如，传感器或执行器故障、警报系统不起作用、控制器调整或配置不当等故障可能会使最复杂的控制系统无法使用。这种计划外的中断可能会造成每天超过100万美元的损失，平均而言，它们夺去了工厂7%的年产能。 新颖性：虽然统计推理一直是搜索工具的主要引擎，但这些相同的方法在流程工业中并没有得到有效的使用。这项建议的主要目标是开发一个理论框架，以便使用统计推断方案从存档的过程数据中获取模型，并随后使用这些模型在不确定的条件下作出决定。具体目标是制定如何处理特定故障的方法，并根据过程连通性信息制定危险可操作性指南，这些信息可以从过程数据中捕获，并使用管道和仪表图表中的信息进行验证。 重大意义：这一提议的社会经济效益将是一种安全、高生产率和高能效的过程监测方法，适用于否则难以建模和操作的过程。