Intelligent Systems for Malfunction Diagnosis and Response

在运行中的化工厂中，通过监控过程变量并将其波动控制在所需范围内来维持产品质量。当操作条件变化超出这些设计限制时，不仅产品质量会受到威胁，而且如果不加以纠正，这些变化可能会导致灾难性事件，如爆炸、火灾或有毒化学物质的释放。该项目的目标是研究开发供工厂操作员使用的用于在线诊断过程故障的基于计算机的系统。计算机自动诊断已经在许多领域得到了研究。然而，有几个特征区分了化工厂诊断问题：(1)状态描述涉及依赖时间的连续变量和离散变量，(2)存在具有物质、能量和信息流的反馈和前馈的复杂流拓扑，(3)复杂的动态与故障响应相关联，(4)故障包括诸如副反应的化学效应，机械故障，如管道中的阀门卡住和堵塞，以及信息处理故障，如传感器，可能会对相同的故障产生一系列响应类型。(6)诊断是基于对易出错的传感器的在线测量，测量参数。这项研究将创造新的机制，将植物特有的和一般的行为知识与诊断方法结合起来。PI计划在四个子领域进行研究：(1)创建和改进涉及诊断和操作员建议的对象的正式表示，包括：a.物理实体，如工厂单元，B.抽象对象，如对正常行为和系统预期功能的约束；c.概念性对象，如故障假设、推断故障、异常事件和解释。(2)根据这些表示法，开发机组运行模型库和链接这些组件的机制，以形成特定工厂的因果拓扑和预期功能的模型。(3)开发一种机制，用于解释基本流程图模型并识别潜在的重要实时事件、其原因和关系。(4)对动态推理进行了研究，提出了一种解释实时事件序列的通用推理机制。将开发包含真实特征和故障范围的过程工厂的动态数学模型。这将允许对表示和方法进行测试，并调查过程/模型不匹配、噪声和无序警报的影响。建筑将被创造出来，工业设计工程师可以将特定于植物的知识融入其中。

In an operating chemical plant, product quality is maintained by monitoring process variables and controlling their fluctuations within a desired range. When operating conditions vary outside these design limits, not only is the product quality in jeopardy, but if left uncorrected, these variations could result in a catastrophic event such as an explosion, fire, or the release of toxic chemicals. The objective of this project is research leading to the development of computer-based systems for on-line diagnosis of process malfunctions to be used by plant operators. Computer-automated diagnosis has been studied in many domains. However, several features distinguish the chemical plant diagnosis problem: (1) the state description involves time-dependent continues and discrete variables, (2) there are complex stream topologies with feedback and feedforward flows of material, energy and information, (3) complex dynamics are associated with malfunction response, (4) malfunctions include chemical effects such as side reactions, mechanical faults such as valves sticking and blockages in pipes, and information processing faults such as sensor vary, possibly producing a family of response types for the same malfunction, (6) the diagnosis is based on on-line measurements with fallible sensors, parameters are measured. This research will create new mechanisms for joining plant-specific and general behavioral knowledge with diagnostic methodology. The PI plans to do research in four subareas: (1) Create and refine formal representation of the objects involved in diagnosis and operator advising, including: a. Physical entities such as plant units, b. Abstract objects such as constraints on normal behavior and system intended functions, and c. Conceptual objects such as malfunction hypotheses, inferred malfunctions, abnormal events, and explanations. (2) Based on these representations, develop a library of unit operation models and a mechanism to link these components to form models of the causal topology and intended functions of specific plants. (3) Develop a mechanism for interpreting the basic flowsheet model and identifying potentially significant real-time events, their causes and relationships. (4) Conduct research on dynamic reasoning leading to the development of a general inference mechanism for interpreting real-time event sequences. Dynamic mathematical models of process plants incorporating realistic features and a board range of malfunctions will be developed. This will permit testing of the representations and methodologies, and the investigation of process/model mismatch, the effects of noise and of out-of-order alarms. Architecture will be created into which plant- specific knowledge can be incorporated by industrial design engineers.