U.S.-Egypt Joint Cooperative Research: Diagnosis and Maintenance of Relay Ladder Logic programs and PLC Ladder Logic Diagrams Using Artificial Neural Networks

美埃联合合作研究：利用人工神经网络诊断和维护继电器梯形逻辑程序和PLC梯形逻辑图

• 批准号: 0515701
• 负责人: Houshang Darabi
• 金额: $ 2.84万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515701&HistoricalAwards=false
• 关键词: U.S.; Egypt; Joint; Cooperative; Research

0515701DarabiProject Description: This project supports a collaborative research between Dr. Houshang Darabi, Mechanical and Industrial Engineering at the University of Illinois, Chicago and Dr. Magdy Abdelhameed, Ain Shams University. They plan to focus on addressing the problem of rehabilitation and debugging of Ladder Logic Diagram (LLD) and Relay Ladder Logic (RLL) controllers. They seek to create methods and tools that use artificial intelligence systems to solve the problems of RLL and LLD based controllers. They propose a controller design based on an Artificial Neural Network (ANN) approach to be used as an alternative controller, and as a debugger for LLD faults. It also rehabilitates the RLL control circuits. They will use Recurrent Neural Network (RNN) based controllers for PLC program diagnosis.Intellectual Merit: Discrete event manufacturing systems represent more than ninety percent of the manufacturing and production lines in our world. RLL as logic controllers were used in old discrete event control systems while programmable logic controllers (PLC) with LLD as their interfacing programming language are utilized in modern discrete event control systems. However, RLL as a controller has many pitfalls. One of them is that once a RLL controller is made, it is very costly and in some cases it is even impossible to modify the controller. This means that a RLL is inflexible and therefore it is not suitable to design flexible manufacturing systems. LLD is not inflexible - changing a LLD can be done quickly. However LLD has its own deficiency - it is hard to debug and maintain real world LLDs. This is due to many factors such as non-structured nature of LLD, the LLD programmers' background, and the huge sizes of real world LLD. This research effort focuses on addressing the problem of rehabilitation and debugging of LLD and RLL controllers. The PIs seek to create methods and tools that use artificial intelligence systems to solve the stated problems of RLL and LLD based controllers. They propose a controller design based on an ANN approach. The resulting controller is used as an alternative controller, and as a debugger for LLD faults. It also rehabilitates the RLL control circuits. They specifically use Recurrent Neural Network (RNN) based controllers for PLC program diagnosis. The PIs present a manufacturing system example to illustrate the applicability of the proposed algorithms. The RNN is trained with sufficient data including the program sequences, faults and history of the events. Extensive experiments will be carried out to test and validate the proposed intelligent ANN based logic controller and an LLD debugger.Broader Impact: This project is expected to have an enormous impact on current manufacturing systems that use PLCs and RLL as their main controller hardware. The techniques of the RNN based controllers developed in this project will have many advantages including quick and efficient diagnosis, and low-cost maintenance of the existing PLC LLD programsespecially when these programs are large in size and can be hardly modified. This project is being supported under the US-Egypt Joint Fund Program, which provides grants to scientists and engineers in both countries to carry out these cooperative activities.

0515701 Darabi项目描述：该项目支持芝加哥伊利诺伊大学机械与工业工程博士Houshang Darabi和艾因夏姆斯大学Magdy Abdelhameed博士之间的合作研究。 他们计划重点解决梯形逻辑图（LLD）和继电器梯形逻辑（RLL）控制器的修复和调试问题。他们寻求创建使用人工智能系统来解决基于RLL和LLD的控制器的问题的方法和工具。他们提出了一种基于人工神经网络（ANN）方法的控制器设计，可用作替代控制器，并作为LLD故障的调试器。它还修复了RLL控制电路。他们将使用基于递归神经网络（RNN）的控制器进行PLC程序诊断。智能优势：离散事件制造系统代表了世界上90%以上的制造和生产线。传统的离散事件控制系统采用RLL作为逻辑控制器，而现代的离散事件控制系统则采用LLD作为接口编程语言的可编程逻辑控制器（PLC）。然而，RLL作为一个控制器有很多缺陷。其中之一是，一旦RLL控制器制成，它是非常昂贵的，在某些情况下，甚至不可能修改控制器。这意味着RLL是不灵活的，因此不适合设计柔性制造系统。 LLD不是一成不变的-更改LLD可以很快完成。然而LLD也有其自身的不足之处--难于调试和维护真实的世界的LLD。这是由于许多因素，如LLD的非结构化性质，LLD程序员的背景，以及真实的世界LLD的巨大规模。 这项研究工作的重点是解决问题的康复和调试的LLD和RLL控制器。PI寻求创建使用人工智能系统来解决基于RLL和LLD的控制器的所述问题的方法和工具。他们提出了一种基于人工神经网络方法的控制器设计。所得到的控制器被用作替代控制器，并作为LLD故障的调试器。它还修复了RLL控制电路。他们专门使用基于递归神经网络（RNN）的控制器进行PLC程序诊断。PI提出了一个制造系统的例子来说明所提出的算法的适用性。RNN使用足够的数据进行训练，包括程序序列，故障和事件历史。将进行广泛的实验，以测试和验证所提出的智能人工神经网络为基础的逻辑控制器和LLD debugger.Broader影响：该项目预计将有一个巨大的影响，目前的制造系统，使用PLC和RLL作为其主要控制器硬件。在本项目中开发的基于RNN的控制器技术将具有许多优点，包括快速有效的诊断，以及现有PLC LLD程序的低成本维护，特别是当这些程序规模很大并且很难修改时。该项目得到了美国-埃及联合基金方案的支持，该方案向两国的科学家和工程师提供赠款，以开展这些合作活动。