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首页> 外文期刊>Computers & Industrial Engineering >A novel automata and neural network based fault diagnosis system for PLC controlled manufacturing systems
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A novel automata and neural network based fault diagnosis system for PLC controlled manufacturing systems

机译：一种基于自动化和神经网络的PLC控制制造系统故障诊断系统

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摘要

The Fault and Anomaly Detection and Isolation (FADI) in Programmable Logic Controller (PLC) controlled systems is an important and challenging problem. In this paper, we present an automated tool, called the Manufacturing Process Failure Diagnosis Tool (MPFDT) that can detect and isolate the faults and anomalies in the PLC controlled manufacturing systems effectively. MPFDT utilizes two independent knowledge-based process behaviour models of the manufacturing system to satisfy the FADI purpose. The fundamental idea is to detect the inconsistencies between the modelled and the observed manufacturing process behaviour. The first model is a Deterministic Finite-state Automaton (DFA) based model of the PLC control process that is used to determine whether the observed state transition behaviour of the PLC control process is consistent with the modelled state transition behaviour or not. The second model is basically a set of Artificial Neural Network (ANN) based one-class classifiers that are used to identify whether any significant difference exist between the observed and the reference electrical power consumption profile of the manufacturing system or not. The experimental results show that the FADI accuracy rate of the proposed tool is very high (more than 98%).
机译：可编程逻辑控制器（PLC）控制的系统中的故障和异常检测与隔离（FADI）是一个重要且具有挑战性的问题。在本文中，我们介绍了一种称为制造过程故障诊断工具（MPFDT）的自动化工具，该工具可以有效地检测和隔离PLC控制的制造系统中的故障和异常。 MPFDT利用制造系统的两个独立的基于知识的过程行为模型来满足FADI的目的。基本思想是检测建模和观察到的制造过程行为之间的不一致。第一个模型是PLC控制过程的基于确定性有限状态自动机（DFA）的模型，该模型用于确定观察到的PLC控制过程的状态转移行为是否与建模的状态转移行为一致。第二个模型基本上是一组基于人工神经网络（ANN）的一类分类器，用于识别制造系统的观测和参考功耗曲线之间是否存在任何重大差异。实验结果表明，该工具的FADI准确率非常高（超过98％）。

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