Intelligent Systems for Malfunction Diagnosis and Response

故障诊断和响应的智能系统

• 批准号: 8814226
• 负责人: Mark Kramer
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-09-15 至 1992-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8814226&HistoricalAwards=false
• 关键词: Intelligent; Systems; Malfunction; Diagnosis; Response

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.

在一个运行的化工厂中，产品质量是通过以下方式来保持的： 监控过程变量并控制其波动 在所需的范围内。 当外部运行条件发生变化时 这些设计限制，不仅是产品质量处于危险之中， 但如果不加以纠正，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。 该项目的目的是研究 导致开发基于计算机的在线系统， 工厂操作员使用的过程故障诊断。 计算机自动诊断已经在许多领域进行了研究。 然而，有几个特点区分化工厂 诊断问题：（1）状态描述涉及 时间依赖的连续和离散变量，（2）有 具有反馈和前馈流的复杂流拓扑， 物质、能量和信息，（3）复杂动力学， 与故障响应相关，（4）故障包括 化学效应，如副反应，机械故障， 如阀门卡住和管道堵塞，以及信息 处理故障，如传感器变化，可能会产生 同一故障的响应类型族，（6） 诊断是基于用易出错的传感器进行的在线测量， 参数进行了测量。 这项研究将创造新的 将植物特异性和一般行为 知识与诊断方法。 PI计划在四个子领域进行研究： (1)创建和细化对象的形式化表示 参与诊断和操作员建议，包括： a. 物理实体，如工厂单位， B. 抽象对象，如法线上的约束 行为和系统预期功能，以及 C. 概念对象，如故障假设， 推断故障、异常事件，以及 解释。 (2)基于这些表示，开发一个单元库 运作模式和机制，以连接这些 组成部分，以形成因果拓扑结构的模型， 特定植物的功能。 (3)建立解释基本流程图的机制 建模和识别潜在的重要实时 事件、原因和关系。 (4)进行动态推理研究， 开发通用推理机制， 解释实时事件序列。 过程工厂的动态数学模型 现实的功能和董事会的故障范围将是 开发这将允许测试陈述， 方法，以及对过程/模型不匹配的调查， 噪声和故障报警的影响。 架构 将被创造出来，植物特有的知识可以被 由工业设计工程师组成。