Process Fault Diagnosis and Control Using Neural Networks

该项目的主要目标是研究和 展示了一种基于神经网络的新方法 面向过程故障的网络设计 检测和控制系统。 过去的做法 故障诊断系统没有正确地包括 人类专家的推理策略和经验 因此，在有效和正确的 解决问题 这个项目，将探索一个小说 方法使用神经网络，解决 重要问题，这些问题是 开发基于知识的工艺系统 故障诊断与控制 我们的方法 开发基于知识的系统依赖于 神经网络的重要特征，即， 他们自动分类和学习的能力 输入和输出数据之间的关联， 能够处理噪声数据。 私家侦探已经 在一个典型案例中成功地测试了他的方法 研究涉及CSTR及其支持子系统。 他计划进一步研究这种方法， 实验更大的化学原型 过程工厂以及诊断动态 过程故障。 他还建议分析 这种方法对传感器故障、噪声 和不确定的数据，以及不完整的信息， 症状 还建议调查使用 神经网络来模拟行为过程 单位及其控制。 此外，他建议 研究神经网络与 基于知识的技术。

The major goal of this project is to research and demonstrate a new approach based on neural networks towards the design of process fault detection and control systems. Past approaches in fault diagnostic systems did not properly include the human expert's reasoning strategies and experience and hence were not adequate in efficient and correct trouble-shooting. This project, will explore a novel method using neural networks that address the important issues which are central to the development of knowledge-based systems for process fault diagnosis and control. Our method for developing knowledge-based systems rests on the important characteristics of neural networks, namely, their ability to automatically classify and learn associations between input and output data and to be able to handle noisy data. The P.I. has already successfully tested his approach on a prototypical case study involving a CSTR and its support subsystems. He plans to investigate this approach further by experimenting with larger prototypes of chemical process plants as well as for diagnosing dynamic process faults. He also proposes to analyze the robustness of this approach to sensor failures, noisy and uncertain data, and incomplete information on symptoms. It is also proposed to investigate the use of neural networks for modeling the behavior process units and their in control. Furthermore, he proposes to investigate the integration of neural nets with knowledge-based techniques.