https://hal.archives-ouvertes.fr/hal-00147431/document

Manufacturing plant control: Challenges and issues

Programmable logic controllers (PLCs) are heavily used in industrial control systems, because of their high capacity of simultaneous input/output processing capabilities. Characteristically, PLC systems are used in mission critical systems, and PLC software needs to conform real-time constraints in order to work properly. Since PLC programming requires mastering low-level instructions or assembly like languages, an important step in PLC software production is modelling using a formal approach like Petri nets or automata. Afterward, PLC software is produced semiautomatically from the model and refined iteratively. Model checking, on the other hand, is a well-known software verification approach, where typically a set of timed properties are verified by exploring the transition system produced from the software model at hand. Naturally, model checking is applied in a variety of ways to verify the correctness of PLC-based software. In this paper, we provide a broad view about the difficulties that are encountered during the model checking process applied at the verification phase of PLC software production. We classify the approaches from two different perspectives: first, the model checking approach/tool used in the verification process, and second, the software model/source code and its transformation to model checker's specification language. In a nutshell, we have mainly examined SPIN, SMV, and UPPAAL-based model checking activities and model construction using Instruction Lists (and alike), Function Block Diagrams, and Petri nets/automata-based model construction activities. As a result of our studies, we provide a comparison among the studies in the literature regarding various aspects like their application areas, performance considerations, and model checking processes. Our survey can be used to provide guidance for the scholars and practitioners planning to integrate model checking to PLC-based software verification activities.

An overview of model checking practices on verification of PLC software

This paper deals with the identification of discrete-event manufacturing systems that are automated using a programmable logic controller (PLC). The behavior of the closed-loop system (PLC and Plant) is observed during its operation and is represented by a single long sequence of observed input/output (I/O) signals vectors. The proposed method follows a black-box and passive identification approach that allows addressing large and complex industrial DES and yields compact and expressive interpreted Petri net (IPN) models. It consists of two complementary stages; the first one obtains, from the I/O sequence, the reactive part of the model composed by observable places and transitions. The I/O sequence is also mapped into a sequence of the created transitions, from which the second stage builds the non observable part of the model including places that ensure the reproduction of the observed input output sequence. This method, based on polynomial-time algorithms on the size of the input data, has been implemented as a software tool that generates and draws the IPN model; it has been tested with input/output sequences obtained from real systems in operation. The tool is described and its application is illustrated through a case study.

/pdf/a-black-box-identification-method-for-automated-discrete-aeu4mbol4i.pdf

A Black-Box Identification Method for Automated Discrete-Event Systems

Most model-based approaches to fault diagnosis of discrete-event systems (DESs) require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In this paper, we address the problem of diagnosing faults, given an incomplete model of the discrete-event system. When the model is incomplete, discrepancies will arise between the actual output and the output predicted by the model. We introduce learning into the diagnoser construction by forming hypotheses that explain these discrepancies. We view the process of generating and evaluating hypotheses about the model of the system as an instance of the set-cover problem, which we formalize using parsimonious covering theory. We describe in detail the construction of the learning diagnoser, which not only performs fault diagnosis but also attempts to learn the missing model information. If the model is complete, the learning diagnoser reduces to the standard state-based diagnoser. Examples are provided to illustrate how learning and diagnosis can be simultaneously achieved through the learning diagnoser.

Fault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning

This paper proposes a data-driven method to determine concurrent parts in Discrete Event Systems (DES). The aim is to improve the results of black-box identification methods without considering any system information except of observed data. To allow an analysis of the collected data, the impact of concurrency on the exhibited system data is determined by two criteria. We propose to use an optimization algorithm that isolates concurrent parts of the system by minimizing concurrency expressed by the two proposed criteria within the determined subsystems. A lab-size application shows the potential of the method for real-world manufacturing systems. The aim is to deliver optimal identified models for fault detection and isolation.

/pdf/black-box-identification-of-discrete-event-systems-with-4h5rumphk1.pdf

Black-box identification of discrete event systems with optimal partitioning of concurrent subsystems

In this paper a graphical editor to design Programmable Logic Controller (PLC) programs using Signal Interpreted Petri Nets (SIPN) is presented. SIPN are an extension of condition event Petri nets that allow the handling of input and output signals. The presented tool, SIPN Editor, has been developed using DiaGen which is an environment for rapidly developing diagram editors from a formal specification of the diagram language. The SIPN Editor supports the translation of SIPN into input code for the model checker SMV. Using SMV, the SIPN can be verified before it is automatically translated into Instruction List code according to the IEC 61131-3 standard. This code can be downloaded on nearly every PLC.

PLC programming with signal interpreted Petri nets

An overview on the different steps involved in the development of a logic control algorithm from the informal specification to the final implementation on a programmable logic controller (PLC) is given. Based on this overview the steps in the development process are presented in detail. An example is used throughout the paper to illustrate the methods. The approach uses Signal Interpreted Petri Nets for the formal description of control algorithms, symbolic model checking for Verification and Validation, and automatic code generation in Instruction List according to IEC 61131-3 for implementation.

A Petri Net based Approach to the Development of correct Logic Controllers

Designing fault-tolerant controllers using SIPN and model-checking

In this paper the advantages of verification and validation to support changes of an existing PLC program are shown. The controller is defined using Signal Interpreted Petri Nets (SIPN) and verification and validation are performed using symbolic model-checking. The main focus of this paper is to show the process and the benefits of verification and validation for the reliability of the control algorithm when specified changes are to make. This is clarified by the example of a heating tank controller throughout the text.

https://hal.archives-ouvertes.fr/hal-00425210/document

Supporting the changeability of SIPN-based logic control algorithms by verification and validation

This article presents a method to identify logic discrete event systems composed of a controller and a plant running in a closed loop Since the identified model will be used for fault detection, the quality of the identification is a major issue In order to quantify the accuracy between the identified model and the observed behaviour of the system, a behavioural quality metric is defined Furthermore, the size of the identified model must be compatible for an online use Therefore, two criteria that define the structural complexity of the identified model are introduced Thanks to these criteria, the proposed identification algorithm allows defining the accuracy of the identified model Finally experimental results are presented and discussed

/pdf/identification-comportementale-des-systemes-logiques-en-vue-1kbnfb3tm6.pdf

Identification comportementale des systèmes logiques en vue de leur surveillance

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Stéphane Klein

Author Tools

Chat about Author