Plant model generation for PLC simulation

Park, Hyeongtae; Kwak, Jong-Geun; Wang, Gi-Nam; Park, Sang C.

doi:10.1080/00207540802577961

Cited by 48 publications

(25 citation statements)

References 14 publications

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“…To cope with the difficulties, there have been studies on methodologies assisting the construction procedure of the logical model [3,58]. Park et al developed a naming rule for PLC symbols so that the symbol names include sufficient information on the logical aspect of the plant model [3]. They presented an example where the logical model of a virtual device was generated from PLC symbol names.…”

Section: Logical Device Modelingmentioning

confidence: 99%

“…Modern manufacturing system is highly integrated and consists of automated workstations. Workstation may have robots with tool-changing capabilities, handling systems, storage systems, and computer control system [1][2][3]. Since manufacturing system requires a heavy investment, a manufacturing system has to be designed so that the long-term profits should remain positive.…”

Section: Introductionmentioning

confidence: 99%

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Survey on the virtual commissioning of manufacturing systems

Lee

Park

2014

Journal of Computational Design and Engineering

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137

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This paper reviews and identifies issues in the application of virtual commissioning technology for automated manufacturing systems. While the real commissioning of a manufacturing system involves a real plant system and a real controller, the virtual commissioning deals with a virtual plant model and a real controller. The expected benefits of virtual commissioning are the reduction of debugging and correction efforts during the subsequent real commissioning stage. However, it requires a virtual plant model and hence still requires significant amount time and efforts. Two main issues are identified, the physical model construction of a virtual device, and the logical model construction of a virtual device. This paper reviews the current literature related to the two issues and proposes future research directions to achieve the full utilization of virtual commissioning technology.

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Section: Logical Device Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey on the virtual commissioning of manufacturing systems

Lee

Park

2014

Journal of Computational Design and Engineering

Self Cite

137

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“…Koo et al (2010) propose a framework for verification of PLC logic through modelling and simulation. Park et al (2010) present a systematic procedure for generating a plant model from the symbol table of a PLC program. Li and Jiang (2007) propose an approach to efficiently control virtual production systems (VPSs).…”

Section: Related Workmentioning

confidence: 99%

A computational control implementation environment for automated manufacturing systems

Diogo

Santos

Vieira

et al. 2012

International Journal of Production Research

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2012) A computational control implementation environment for automated manufacturing systemsThis paper presents a computational environment as a tool for supporting the implementation of control coding of an automated manufacturing system. The proposed environment considers a cyclic three-stage control development -modelling, synthesis and implementation -until the real system accomplishes the required specification, resulting in the automated and integrated manufacturing system. The research details the three stages and describes the steps executed for each one. The mathematical formalism used in this work is also presented, as a basis for control implementation. The implementation environment is proposed in order to validate the control structure of this formalism and to allow a progressive integration of control hardware and software. To submit to a test and validate the proposal environment, two experiments are performed, in two different manufacturing systems. Thus, it is demanded that the control system can be reconfigurable in a fast and reliable way.

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“…In literature, PLC controlled device behaviour is usually represented by a state-based I/O model or by using a similar kind of automaton model [6, 21, 22]. A state-based I/O model of a device actually expresses the functional role of its control signals.…”

Section: Plat: Control Process Model Design and Working Proceduresmentioning

confidence: 99%

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

Ghosh

Qin

Lee

et al. 2016

Computational Intelligence and Neuroscience

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Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

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Plant model generation for PLC simulation

Cited by 48 publications

References 14 publications

Survey on the virtual commissioning of manufacturing systems

Survey on the virtual commissioning of manufacturing systems

A computational control implementation environment for automated manufacturing systems

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

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