scholarly journals Unavailability of K-out-of-N: G Systems with non-identical Components Based on Markov Model

2019 ◽  
Vol 2 (1) ◽  
pp. 25-35
Author(s):  
Ayodeji Akinsoji Okubanjo ◽  
Olasunkami oriola Akinyemi ◽  
Oluwadamilola Kehinde Oyetola ◽  
Olawale omopariola Olaluwoye ◽  
Olufemi Peter Alao
Keyword(s):  
Markov Model ◽  
Probability Of Failure ◽  
Process Industry ◽  
Redundant System ◽  
System Architectures ◽  
On Demand ◽  
Proposed Model ◽  
Challenging Tasks ◽  
Average Probability ◽  
Analytical Formulas

The process industry has always been faced with the challenging tasks of determining the overall unavailability of safety instrumented systems (SISs). The unavailability of the safety instrumented system is quantified by considering the average probability of failure on demand. To mitigate these challenges, the IEC 61508 has established analytical formulas for estimating the average probability of failure on demand for K-out-of-N (KooN) architectures. However, these formulas are limited to the system with identical components and this limitation has not been addressed in many researches. Hence, this paper proposes an unavailability model based on Markov Model for different redundant system architectures with non-identical components and generalised formulas are established for non-identical k-out-of-n and n-out-of-n configurations. Furthermore, the proposed model incorporates undetected failure rate and evaluates its impact on the unavailability quantification of SIS. The accuracy of the proposed model is verified with the existing unavailability methods and it is shown that the proposed approach provides a sufficiently robust result for all system architectures.  

scholarly journals Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Ahmed H. Aburawwash ◽  
Moustafa Mohammed Eissa ◽  
Azza F. Barakat ◽  
Hossam M. Hafez
Keyword(s):  
Genetic Algorithm ◽  
Accurate Determination ◽  
Probability Of Failure ◽  
System Structure ◽  
Control Element ◽  
Algorithm Optimization ◽  
On Demand ◽  
Uncertainty Sources ◽  
Iec 61508 ◽  
Proposed Model

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced.

The Effects of Maintenance Actions on the Average Probability of Failure on Demand of Spring Operated Pressure Relief Valves

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Julia V. Bukowski ◽  
William M. Goble ◽  
Robert E. Gross ◽  
Stephen P. Harris
Keyword(s):  
Probability Of Failure ◽  
Pressure Relief ◽  
Periodic Inspection ◽  
On Demand ◽  
Plant Safety ◽  
Safety Integrity Level ◽  
Proof Testing ◽  
Average Probability ◽  
Pressure Relief Valves ◽  
Inspection And Maintenance

The safety integrity level (SIL) of equipment used in safety instrumented functions is determined by the average probability of failure on demand (PFDavg) computed at the time of periodic inspection and maintenance, i.e., the time of proof testing. The computation of PFDavg is generally based solely on predictions or estimates of the assumed constant failure rate of the equipment. However, PFDavg is also affected by maintenance actions (or lack thereof) taken by the end user. This paper shows how maintenance actions can affect the PFDavg of spring operated pressure relief valves (SOPRV) and how these maintenance actions may be accounted for in the computation of the PFDavg metric. The method provides a means for quantifying the effects of changes in maintenance practices and shows how these changes impact plant safety.

Rams Analysis for Different XT Technologies

2021 ◽  
Author(s):  
Markus Glaser ◽  
Tobias Winter
Keyword(s):  
Probability Of Failure ◽  
Hydraulic Actuator ◽  
System Architectures ◽  
Electric Actuator ◽  
Availability Analysis ◽  
On Demand ◽  
Roller Screw ◽  
Critical Components ◽  
Valve Operation ◽  
Reliability And Availability

Abstract This paper analyses the probability of failure on demand of different subsea christmas tree actuation principles and their related control system architectures. The all-electric technology has limited or insufficient field data available. This means that the reliability and availability analysis is based on theoretical analysis from data provided in reliability handbooks for mechanical and electronic components. The analysis includes the probability of failure on demand to isolate the well and the availability of each equipment type until a first failure causes the need for repair. The following different actuator and system designs were chosen for this analysis: – Spring based hydraulic actuator – Spring based electric actuator – Electric power screw actuator – Electric planetary roller screw actuator All Electric Systems (except the spring based electric actuator) utilize a battery to provide the energy for the valve operation. The reliability analysis provides detailed information about the major contributors that limit the reliability of the actuators and systems. With this knowledge, qualification activities can focus on the improvement of the reliability of the critical components and the actuator elements within the system. The power screw actuator and the corresponding system provides the best reliability and availability compared to other systems. The electric with spring design provides better results than the hydraulic with spring design. Generally, the battery-based systems provide a better reliability than spring-based designs. The most critical elements are the mechanical springs, sealings, brakes and the spindle mechanisms. Another aspect is the analysis of an optimized operation strategy in order to utilize the redundant components to improve the availability and reduce the number of interventions by analysis of the second and third failure in the system.

scholarly journals Average probability of failure on demand estimation for burner management systems

2018 ◽  
Vol 36 (4) ◽  
pp. 1218
Author(s):  
A.A. Okubanjo ◽  
O.K. Oyetola ◽  
A Groot ◽  
A.J. Degraaf
Keyword(s):  
Demand Estimation ◽  
Probability Of Failure ◽  
Management Systems ◽  
On Demand ◽  
Average Probability

Projecting demands for renal replacement therapy in the Northern Territory: a stochastic Markov model

2018 ◽  
Vol 42 (4) ◽  
pp. 380 ◽  
Author(s):  
Jiqiong You ◽  
Yuejen Zhao ◽  
Paul Lawton ◽  
Steven Guthridge ◽  
Stephen P. McDonald ◽  
...  
Keyword(s):  
Renal Replacement Therapy ◽  
Markov Model ◽  
Replacement Therapy ◽  
Management Practices ◽  
Markov Models ◽  
Self Care ◽  
Potential Effect ◽  
Northern Territory ◽  
Baseline Scenario ◽  
On Demand

Objective The aim of the present study was to evaluate the potential effects of different health intervention strategies on demand for renal replacement therapy (RRT) services in the Northern Territory (NT). Methods A Markov chain simulation model was developed to estimate demand for haemodialysis (HD) and kidney transplantation (Tx) over the next 10 years, based on RRT registry data between 2002 and 2013. Four policy-relevant scenarios were evaluated: (1) increased Tx; (2) increased self-care dialysis; (3) reduced incidence of end-stage kidney disease (ESKD); and (4) reduced mortality. Results There were 957 new cases of ESKD during the study period, with most patients being Indigenous people (85%). The median age was 50 years at onset and 57 years at death, 12 and 13 years younger respectively than Australian medians. The prevalence of RRT increased 5.6% annually, 20% higher than the national rate (4.7%). If current trends continue (baseline scenario), the demand for facility-based HD (FHD) would approach 100 000 treatments (95% confidence interval 75 000–121 000) in 2023, a 5% annual increase. Increasing Tx (0.3%), increasing self-care (5%) and reducing incidence (5%) each attenuate demand for FHD to ~70 000 annually by 2023. Conclusions The present study demonstrates the effects of changing service patterns to increase Tx, self-care and prevention, all of which will substantially attenuate the growth in FHD requirements in the NT. What is known about the topic? The burden of ESKD is projected to increase in the NT, with demand for FHD doubling every 15 years. Little is known about the potential effect of changes in health policy and clinical practice on demand. What does this paper add? This study assessed the usefulness of a stochastic Markov model to evaluate the effects of potential policy changes on FHD demand. What are the implications for practitioners? The scenarios simulated by the stochastic Markov models suggest that changes in current ESKD management practices would have a large effect on future demand for FHD.

scholarly journals A Markov-Based Intrusion Tolerance Finite Automaton

2021 ◽  
Vol 28 (2) ◽  
pp. 89-100
Keyword(s):  
Network Security ◽  
Markov Model ◽  
Finite Automaton ◽  
Network Services ◽  
Intrusion Tolerance ◽  
Security Strategy ◽  
Automatic Learning ◽  
Learning Mechanism ◽  
Proposed Model ◽  
Tolerance Model

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton.

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