Probabilistic error propagation model for mechatronic systems

Morozov, Andrey; Janschek, Klaus

doi:10.1016/j.mechatronics.2014.09.005

Cited by 28 publications

(4 citation statements)

References 31 publications

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“…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%

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

Fabarisov¹,

Yusupova²,

Ding³

et al. 2020

ACTA POLYTECH HUNG

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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.

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

confidence: 99%

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

Fabarisov¹,

Yusupova²,

Ding³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

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“…The interaction of different components should be concerned with information from both. In [7], the authors present an error model aimed at Operating Systems, which is a transient data level error. They pay more attention to the error type and the influence of the errors.…”

Section: Error Propagation Analysismentioning

confidence: 99%

“…Here, a problem arises: How can we calculate the reliability of the entire system given the reliability of every path? In [7], Andrey Morozov and Klaus Janschek use control flow and data flow graphs to predict the probability of different paths occurring. Nevertheless, in [10], the authors take advantages of UML diagrams to build the reliability model.…”

Section: Case Studymentioning

confidence: 99%

“…In this article, we comply with the Markovian assumption in component-based system analysis that the next state of the system is only affected by the current state. Morozov et al [7] propose a Markov model mainly to estimate the error propagation probability from one component to another in a system. Their approach makes it possible to investigate the error propagation features in a system.…”

Section: Introductionmentioning

confidence: 99%

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A Markov Error Propagation Model for Component-based Software Systems

Tian¹,

Wang²,

Zong³

2018

IJPE

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In this paper, we propose a Markov chain-based error propagation model to analyze the reliability of component-based software systems and take measures to make the critical components safer. Because it is difficult to test the whole component-based system, we apply an error propagation model to evaluate the reliability of the system with parameters obtained by preliminary data from existing components and integration testing from two connected components. The main parameters required in our Markov model are the error probability for each component, the error tolerance probability, and the error propagation probability for every two connected components. Our model is applied to compute the reliability of the system, find the most suspicious component during debugging, and protect the critical components. Finally, we simulate the process of these three applications using three different systems on MATLAB.

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