AADL-Based Stochastic Error Propagation Analysis for Reliable System Design of a Medical Patient Table

Morozov, Andrey; Mutzke, Thomas; Ren, Boqi; Janschek, Klaus

doi:10.1109/ram.2018.8463141

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may outweigh the advantage in accuracy. [26] provide a method only for the stochastic error propagation analysis, however, the nondeterminism is not considered. They transform AADL models to formal Dual-graph Error Propagation Models (DEPMs) for reliability analysis.…”

Section: Discussionmentioning

confidence: 99%

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

et al. 2019

View full text Add to dashboard Cite

As safety-critical systems, grid cyber-physical systems (GCPSs) are required to ensure the safety of power-related systems. However, in many cases, GCPSs may be subject to uncertain and nondeterministic environmental hazards, as well as the variable quality of devices. They can cause failures and hazards in the whole system and may jeopardize system safety. Thus, it necessitates safety analysis for system safety assurance. This paper proposes an architecture-level safety analysis approach for GCPSs applying the probabilistic model-checking of stochastic games. GCPSs are modeled using Architecture Analysis and Design Language (AADL). Random errors and failures of a GCPS and nondeterministic environment behaviors are explicitly described with AADL annexes. A GCPS AADL model including the environment can be regarded as a game. To transform AADL models to stochastic multi-player games (SMGs) models, model transformation rules are proposed and the completeness and consistency of rules are proved. Property formulae are formulated for formal verification of GCPS SMG models, so that occurrence probabilities of failed states and hazards can be obtained for system-level safety analysis. Finally, a modified IEEE 9-bus system with grid elements that are power management systems is modeled and analyzed using the proposed approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

et al. 2019

View full text Add to dashboard Cite

show abstract

“…fmdtools has additionally since expanded in scope to include not just fault propagation tools but the necessary analysis and visualization tools needed to interpret Toolkit Causality Representation Model Format(s) Availability Use in design HiP-Hops (Papadopoulos & McDermid, 1999) Dynamic simulation with failure logic Simulink, Simula-tionX, AADL, etc. Commercial Functional Hazard Assessment, Design Optimization (Papadopoulos et al, 2011) Rodon (Bunus et al, 2009) Behavioral constraint network with failure logic Modelica-like Rodelica model Commercial Model-based engineering process (Lunde et al, 2006) Modelica fault libraries (van der Linden, 2014;Minhas et al, 2014;Gundermann et al, 2019) Undirected behavioral/failure logic Modelica Open Source Design exploration (Lattmann et al, 2014) OpenErrorPro (Morozov et al, 2019) Probabilistic markov chain Simulink, Stateflow, UML, SysML, AADL Open Source Model-based reliable system design (Morozov et al, 2018) SHyFTOO (Chiacchio et al, 2020) Dynamic simulation with probabilistic hybrid fault tree Simulink, MAT-LAB code Open Source Model-based design (Chiacchio et al, 2019) OpenCossan (Patelli et al, 2018) Probabalistic semi-markov transitions and/or external simulation…”

Section: Related Workmentioning

confidence: 99%

Fmdtools

Hulse

Walsh

Dong

et al. 2021

IJPHM

View full text Add to dashboard Cite

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process.

show abstract

“…The DEPM allows the computation of the reliability metrics using underlying Discrete-time Markov chain (DTMC) models. DEPMs can be automatically generated from common CPS models including the Simulink/Stateflow models [19], UML models [20], SysML models [21], AADL [22] models, as well as software source code using LLVM [23]. This allows not only the automated application of our method, but also the analysis of systems developed with a combination of modeling paradigms.…”

Section: Dual-graph Error Propagation Modelmentioning

confidence: 99%

“…The tool also integrates optimization methods against state space explosion of DTMC models such as the automatic nesting algorithm and data flow slicing [25]. The model transformation algorithms allow automatic generation of DEPMs from the baseline system models UML/SysML [21], Simulink/Stateflow [19], AADL [22]. After that OpenErrorPro automatically creates discrete time Markov chain models using the DEPM representation and computes required numerical reliability properties using PRISM software.…”

Section: Dual-graph Error Propagation Modelmentioning

confidence: 99%

“…The DEPM is a powerful analytical instrument for the modeling of a broad set of dependability-related features that influence error propagation processes. The feasibility of the DEPM approach has been witnessed by a number of case studies including navigation system of a mobile robot [27], automated medical patient table motion control [22], robotic software for space-to ground haptic feedback control [18], and embedded flight control software of a UAV [21]. DEPM framework could be applied for CPS in the analysis of the propagation of typical CPS errors from potentially faulty components to technically accessible system variables, e.g.…”

Section: Model-based Stochastic Epa For Cyber-physical Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems

Fabarisov¹,

Yusupova²,

Ding³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

View full text Add to dashboard Cite

Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies that is focusing on the creation of smart factories with the modular structured Cyber-Physical Systems (CPS), in tight cooperation with humans. This trend also implies that the systems become more complex, heterogeneous, and distributed especially their network and software parts. This makes the CPS highly critical subject to failures at different levels, including software, hardware, and human operators. Consequently, ensuring reliable and safe operation under the presence of non-avoidable threats also becomes a more complicated task. The proper analysis of the CPS requires thorough comprehension of both the dependability properties of system components and their interactions as well as structural and behavioral aspects of the complete system. Such an analysis of complex and mutually interlinked system properties puts considerable challenges on appropriate methods for modeling and analysis, as well as, on the related applied software tools. The Dual-graph Error Propagation Model (DEPM), developed in our lab, is a mathematical abstraction of the main future system's properties, which are vital for the determination of the error propagation processes. It is a useful analytical instrument for the evaluation of the influence of particular faults and errors to the overall system behavior. OpenErrorPro is our analytical software tool for stochastic error propagation analysis that supports the DEPM framework. Using OpenErrorPro, a DTMC model could be automatically generated from a DEPM, and the reliability metrics, in addition to, error propagation path, can be computed. This could be implemented for the analysis of the heterogeneous CPS components. The necessary steps for the DEPM framework extension, required for such an implementation, are discussed in this paper.

show abstract

AADL-Based Stochastic Error Propagation Analysis for Reliable System Design of a Medical Patient Table

Cited by 6 publications

References 7 publications

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

Safety Analysis of AADL Models for Grid Cyber-Physical Systems via Model Checking of Stochastic Games

Fmdtools

Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems

Contact Info

Product

Resources

About