Fault Diagnosis for Chemical Process Plants

本研究项目的目标是开发能够检测和解释运行中的化工厂故障的计算机算法，供工厂人员在工厂发生故障时做出操作决策使用。专家系统将被开发出来，它允许过程工程师和操作员积累的经验、判断和启发式被纳入一个自动推理系统。专家系统将通过过程控制计算机访问工厂测量数据，实时应用这些知识。这个系统将以模块化的形式设计，这样就可以分解复杂的装置，单独开发单个单元的计算机逻辑（并在随后的迭代中重新组合）。该项目将解决的具体问题包括：(1)考虑噪声和时变数据等现实因素的干扰定位和识别新算法的理论发展；(2)发展一种基于不确定推理技术的诊断理论和方法，以取代目前基于布尔逻辑的理论；(3)启发式推理与基于模型推理相结合的专家系统架构研究；(4)开展基础研究，制定优化过程仪器的指导方针，以提高新诊断系统的有效性；(5)定性故障模拟的理论研究，为故障对工厂运行的影响提供准确的定性预测；(6)将理论思想在更复杂和现实的仿真中实现，对方法进行检验，并对如何改进和扩展当前的诊断系统给出进一步的见解。

The goal of this research project is to develop computer algorithms that can detect and interpret faults in operating chemical plants, to be used by plant personnel making operating decisions during plant upsets. Expert systems will be developed which allow the accumulated experience, judgment, and heuristics of process engineers and operators to be incorporated into an automated reasoning system. The expert system would apply this knowledge in real time by accessing plant measurements through the process control computer. This system will be designed in modular form so that complex plants can be decomposed and computer logic for individual units be developed separately (and recombined in subsequent iterations). The specific problems that will be addressed in this project include: (1) theoretical development of new algorithms for disturbance location and discrimination which account for realistic factors such as noisy and time-varying data; (2) the development of a theory and methodology of diagnosis based on uncertain reasoning techniques, to replace the current theory based on Boolean logic; (3) research on expert system architectures for integration of heuristic and model-based inferences; (4) basic research leading to development of guidelines for optimal process instrumentation to increase the effectiveness of the new diagnostic systems; (5) a theoretical study of qualitative fault simulation to provide accurate qualitative predictions of the effects of malfunctions on plant operations; and (6) the implementation of theoretical ideas in more complex and realistic simulations, to test methodology, and to give further insight on how to improve and extend the current diagnostic system.