Dynamic flowgraph methodology and its applications

Research output: Book/ReportReportProfessional

Abstract

Dynamic flowgraph methodology (DFM) is method for the reliability analysis of dynamic systems with time-dependencies and feedback loops. As in fault tree analysis, the aim of DFM is to identify which conditions can cause a top event, which can be, for example, the system's failure. DFM has been most often applied to different digital control systems. One reason for this is that a DFM model can represent the interactions between a control system and the controlled process. Components of DFM models are analysed at discrete time points and they can have multiple states. The reason for the development of DFM is that traditional methods, such as fault tree analysis, can describe the system's dynamic behaviour only in a limited manner. DFM can more accurately represent system's evolution in time. This report gives an overview of the DFM method and presents the applications of DFM that are found in literature. The application areas include digital control and safety systems in nuclear power plants, space systems, hydrogen production plants, human performance, networked control systems and field programmable gate arrays. In most of the applications, DFM has been used to analyse how control system failures can cause some physical variable, e.g. water level or pressure, to have too low or high value. Generally, DFM has been found useful within the application areas. Most of the presented models have been quite moderately sized, though larger models exist too.
Original languageEnglish
PublisherVTT Technical Research Centre of Finland
Number of pages18
Publication statusPublished - 2017
MoE publication typeD4 Published development or research report or study

Publication series

NameVTT Research Report
PublisherVTT
VolumeVTT-R-03364-16

Fingerprint

Fault tree analysis
Digital control systems
Dynamical systems
Control systems
Networked control systems
Hydrogen production
Reliability analysis
Water levels
Security systems
Nuclear power plants
Field programmable gate arrays (FPGA)
Feedback

Keywords

  • dynamic flowgraph methodology
  • reliability
  • digital systems

Cite this

Tyrväinen, T. (2017). Dynamic flowgraph methodology and its applications. VTT Technical Research Centre of Finland. VTT Research Report, Vol.. VTT-R-03364-16
Tyrväinen, Tero. / Dynamic flowgraph methodology and its applications. VTT Technical Research Centre of Finland, 2017. 18 p. (VTT Research Report, Vol. VTT-R-03364-16).
@book{d7ee3fbc2a5f4a3ea3426f2429147ba3,
title = "Dynamic flowgraph methodology and its applications",
abstract = "Dynamic flowgraph methodology (DFM) is method for the reliability analysis of dynamic systems with time-dependencies and feedback loops. As in fault tree analysis, the aim of DFM is to identify which conditions can cause a top event, which can be, for example, the system's failure. DFM has been most often applied to different digital control systems. One reason for this is that a DFM model can represent the interactions between a control system and the controlled process. Components of DFM models are analysed at discrete time points and they can have multiple states. The reason for the development of DFM is that traditional methods, such as fault tree analysis, can describe the system's dynamic behaviour only in a limited manner. DFM can more accurately represent system's evolution in time. This report gives an overview of the DFM method and presents the applications of DFM that are found in literature. The application areas include digital control and safety systems in nuclear power plants, space systems, hydrogen production plants, human performance, networked control systems and field programmable gate arrays. In most of the applications, DFM has been used to analyse how control system failures can cause some physical variable, e.g. water level or pressure, to have too low or high value. Generally, DFM has been found useful within the application areas. Most of the presented models have been quite moderately sized, though larger models exist too.",
keywords = "dynamic flowgraph methodology, reliability, digital systems",
author = "Tero Tyrv{\"a}inen",
note = "Project code: 102420",
year = "2017",
language = "English",
series = "VTT Research Report",
publisher = "VTT Technical Research Centre of Finland",
address = "Finland",

}

Tyrväinen, T 2017, Dynamic flowgraph methodology and its applications. VTT Research Report, vol. VTT-R-03364-16, VTT Technical Research Centre of Finland.

Dynamic flowgraph methodology and its applications. / Tyrväinen, Tero.

VTT Technical Research Centre of Finland, 2017. 18 p. (VTT Research Report, Vol. VTT-R-03364-16).

Research output: Book/ReportReportProfessional

TY - BOOK

T1 - Dynamic flowgraph methodology and its applications

AU - Tyrväinen, Tero

N1 - Project code: 102420

PY - 2017

Y1 - 2017

N2 - Dynamic flowgraph methodology (DFM) is method for the reliability analysis of dynamic systems with time-dependencies and feedback loops. As in fault tree analysis, the aim of DFM is to identify which conditions can cause a top event, which can be, for example, the system's failure. DFM has been most often applied to different digital control systems. One reason for this is that a DFM model can represent the interactions between a control system and the controlled process. Components of DFM models are analysed at discrete time points and they can have multiple states. The reason for the development of DFM is that traditional methods, such as fault tree analysis, can describe the system's dynamic behaviour only in a limited manner. DFM can more accurately represent system's evolution in time. This report gives an overview of the DFM method and presents the applications of DFM that are found in literature. The application areas include digital control and safety systems in nuclear power plants, space systems, hydrogen production plants, human performance, networked control systems and field programmable gate arrays. In most of the applications, DFM has been used to analyse how control system failures can cause some physical variable, e.g. water level or pressure, to have too low or high value. Generally, DFM has been found useful within the application areas. Most of the presented models have been quite moderately sized, though larger models exist too.

AB - Dynamic flowgraph methodology (DFM) is method for the reliability analysis of dynamic systems with time-dependencies and feedback loops. As in fault tree analysis, the aim of DFM is to identify which conditions can cause a top event, which can be, for example, the system's failure. DFM has been most often applied to different digital control systems. One reason for this is that a DFM model can represent the interactions between a control system and the controlled process. Components of DFM models are analysed at discrete time points and they can have multiple states. The reason for the development of DFM is that traditional methods, such as fault tree analysis, can describe the system's dynamic behaviour only in a limited manner. DFM can more accurately represent system's evolution in time. This report gives an overview of the DFM method and presents the applications of DFM that are found in literature. The application areas include digital control and safety systems in nuclear power plants, space systems, hydrogen production plants, human performance, networked control systems and field programmable gate arrays. In most of the applications, DFM has been used to analyse how control system failures can cause some physical variable, e.g. water level or pressure, to have too low or high value. Generally, DFM has been found useful within the application areas. Most of the presented models have been quite moderately sized, though larger models exist too.

KW - dynamic flowgraph methodology

KW - reliability

KW - digital systems

M3 - Report

T3 - VTT Research Report

BT - Dynamic flowgraph methodology and its applications

PB - VTT Technical Research Centre of Finland

ER -

Tyrväinen T. Dynamic flowgraph methodology and its applications. VTT Technical Research Centre of Finland, 2017. 18 p. (VTT Research Report, Vol. VTT-R-03364-16).