Switching Markov chains for a holistic modeling of SIS unavailability

Mechri, Walid; Simon, Christophe; Benothman, Kamel

doi:10.1016/j.ress.2014.09.005

Cited by 39 publications

(14 citation statements)

References 29 publications

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“…In [12], authors model a safety-related system in low demand mode using Markov chain to calculate PFD measure, in a way that is explained in the respective standard [18]. Several parameters such as CCF, imperfect proof testing, etc.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Analysis of Design Parameters in Safety-Critical Computers

Ahangari

Atik

Ozkok

et al. 2020

IEEE Trans. Emerg. Topics Comput.

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Nowadays, safety-critical computers are extensively used in many civil domains like transportation including railways, avionics, and automotive. In evaluating these safety critical systems, previous studies considered different metrics, but some of safety design parameters like failure diagnostic coverage (C) or common cause failure (CCF) ratio have not been seriously taken into account. Moreover, in some cases safety has not been compared with standard safety integrity levels (IEC-61508: SIL1-SIL4) or even have not met them. Most often, it is not very clear that which part of the system is the Achilles heel and how design can be improved to reach standard safety levels. Motivated by such design ambiguities, we aim to study the effect of various design parameters on safety in some prevalent safety configurations, namely, 1oo2 and 2oo3, where 1oo1 is also used as a reference. By employing Markov modeling, we analyzed the sensitivity of safety to important parameters including: failure rate of processing element, failure diagnostic coverage, CCF ratio, test and repair rates. This study aims to provide a deeper understanding on the influence of variation in design parameters over safety. Consequently, to meet appropriate safety integrity level, instead of improving some parts of a system blindly, it will be possible to make an informed decision on more relevant parameters.

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Section: Related Work and Motivationmentioning

confidence: 99%

Analysis of Design Parameters in Safety-Critical Computers

Ahangari

Atik

Ozkok

et al. 2020

IEEE Trans. Emerg. Topics Comput.

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“…The two approaches are rather simple and visual, they work with static methods and mean values. Other researchers [6,7] use Markov Analysis (MA), a more complex approach that works with dynamic models. A significant contribution to the domain was made by Norwegian researchers who have been developing the PDS method [8].…”

Section: Overview Of the Research Areamentioning

confidence: 99%

Optimization of Safety Instrumented System Design and Maintenance Frequency for Oil and Gas Industry Processes

Redutskiy¹

2017

Management and Production Engineering Review

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Oil and gas industry processes are associated with significant expenditures and risks. Adequacy of the decisions on safety measures made during early stages of planning the facilities and processes contributes to avoiding technological incidents and corresponding losses. Formulating straightforward requirements for safety instrumented systems that are followed further during the detailed engineering design and operations is proposed, and a mathematical model for safety system design is introduced in a generalized form. The model aims to reflect the divergent perspectives of the main parties involved in oil and gas projects, and, therefore, it is formulated as a multi-objective problem. Application of black box optimization is suggested for solving real-life problem instances. A Markov model is applied to account for device failures, technological incidents, continuous restorations and periodic maintenance for a given process and safety system configuration. This research is relevant to engineering departments and contractors, who specialize in planning and designing the technological solution.

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“…Switching Failure Kuo and Ke [36], Shekhar et al [43], Hsu et al [75], Jain and Preeti [78], Jain and Rani [79], Jain et al [81], Jain et al [100], Jain et al [112], Ke et al [122], Jain et al [139]. Common Cause Failure Mechri et al [61], Jain and Gupta [76], Dimou and Economou [92], Jain [99], Jain et al [100], Jain and Gupta [109], Jain et al [139], Maheshwari et al [146].…”

Section: Failures Authorsmentioning

confidence: 99%

A survey on queues in machining system: Progress from 2010 to 2017

Shekhar

Raina

Kumar

et al. 2017

YUGOSLAV J OPERATION

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The aim of the present article is to give a historical survey of some important research works related to queues in machining system since 2010. Queues of failed machines in machine repairing problem occur due to the failure of machines at random in the manufacturing industries, where different jobs are performed on machining stations. Machines are subject to failure what may result in significant loss of production, revenue, or goodwill. In addition to the references on queues in machining system, which is also called 'Machine Repair Problem' (MRP) or 'Machine Interference Problem' (MIP), a meticulous list of books and survey papers is also prepared so as to provide a detailed catalog for understanding the research in queueing domain. We have classified the relevant literature according to a year of publishing, methodological, and modeling aspects. The author(s) hope that this survey paper could be of help to learners contemplating research on queueing domain.

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Switching Markov chains for a holistic modeling of SIS unavailability

Cited by 39 publications

References 29 publications

Analysis of Design Parameters in Safety-Critical Computers

Analysis of Design Parameters in Safety-Critical Computers

Optimization of Safety Instrumented System Design and Maintenance Frequency for Oil and Gas Industry Processes

A survey on queues in machining system: Progress from 2010 to 2017

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