New insight into the average probability of failure on demand and the probability of dangerous failure per hour of safety instrumented systems

2010 ◽  
Vol 224 (2) ◽  
pp. 75-86 ◽  
Author(s):  
F Innal ◽  
Y Dutuit ◽  
A Rauzy ◽  
J-P Signoret
Keyword(s):  
Probability Of Failure ◽  
On Demand ◽  
Average Probability ◽  
Insight Into

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.  

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.

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

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.

ChunkSim

2010 ◽  
pp. 131-149
Author(s):  
Pedro Furtado
Keyword(s):  
Performance Analysis ◽  
Simulation Analysis ◽  
System Size ◽  
Analysis Tool ◽  
Data Allocation ◽  
Data Warehouses ◽  
Availability Analysis ◽  
On Demand ◽  
Self Tuning ◽  
Insight Into

Self-tuning physical database organization involves tools that determine automatically the best solution concerning partitioning, placement, creation and tuning of auxiliary structures (e.g. indexes), based on the workload. To the best of our knowledge, no tool has focused on a relevant issue in parallel databases and in particular data warehouses running on common off-the-shelf hardware in a sharednothing configuration: determining the adequate tradeoff for balancing load and availability with costs (storage and loading costs). In previous work, we argued that effective load and availability balancing over partitioned datasets can be obtained through chunk-wise placement and replication, together with on-demand processing. In this work, we propose ChunkSim, a simulator for system size planning, performance analysis against replication degree and availability analysis. We apply the tool to illustrate the kind of results that can be obtained by it. The whole discussion in the chapter provides very important insight into data allocation and query processing over shared-nothing data warehouses and how a good simulation analysis tool can be built to predict and analyze actual systems and intended deployments.

Mechanistic insight into the singlet oxygen-triggered expansion of hypoxia-responsive polymeric micelles

2018 ◽  
Vol 6 (7) ◽  
pp. 1712-1716 ◽  
Author(s):  
Lina Wang ◽  
Xiaoli Huang ◽  
Bingbing Wang ◽  
Jie Zhao ◽  
Xuliang Guo ◽  
...  
Keyword(s):  
Singlet Oxygen ◽  
Polymeric Micelles ◽  
On Demand ◽  
Mechanistic Insight ◽  
Insight Into

Singlet oxygen can trigger the oxidation of nitroimidazole-bearing micelles for on-demand cargo release.

scholarly journals The Operation Modes of E/E/PE System and Their Influence on Determining and Verifying the Safety Integrity Level

2010 ◽  
Vol 13 (1) ◽  
pp. 289-298
Author(s):  
Tomasz Barnert ◽  
Kazimierz Kosmowski ◽  
Marcin Śliwiński
Keyword(s):  
High Demand ◽  
Related Function ◽  
On Demand ◽  
Safety Integrity Level ◽  
Operation Modes ◽  
Mode Of Operation ◽  
Protection Systems ◽  
Average Probability ◽  
Quantitative Verification ◽  
Probabilistic Criteria

The Operation Modes of E/E/PE System and Their Influence on Determining and Verifying the Safety Integrity Level The standard PN-EN 61508 introduces some probabilistic criteria for the E/E/PE systems that can operate in different modes of operation, which are related to the safety integrity level (SIL). For the control and protection systems, operating in a low demand mode, the criterion is the average probability of dangerous failure on demand PFDavg. In case of systems working in a continuous mode of operation or high demand, the criterion is probability of dangerous failure per hour PFH. In practice, the E/E/PE systems implement many safety-related functions (SRFs), which have different requirements for high and low demands. Thus, there is the problem with choosing proper probabilistic criterion for determining required SIL for a safety-related function to be implemented by these systems as well as in the process of quantitative verification of SIL for considered architectures.

Probability of failure on demand of safety systems: impact of partial test distribution

2012 ◽  
Vol 226 (4) ◽  
pp. 426-436 ◽  
Author(s):  
Florent Brissaud ◽  
Anne Barros ◽  
Christophe Bérenguer
Keyword(s):  
Probability Of Failure ◽  
Test Time ◽  
Time Interval ◽  
System Failures ◽  
On Demand ◽  
Component Failure Rates ◽  
Safety Systems ◽  
The Impact ◽  
Architecture Component ◽  
Approximate Equations

In accordance with the IEC  61508 functional safety standard, safety-related systems operating in a low demand mode need to be proof tested to reveal any ‘dangerous undetected failures’. Proof tests may be full (i.e. complete) or partial (i.e. incomplete), depending on their ability to detect all the system failures or only a part of them. Following a partial test, some failures may then be left latent until the full test, whereas after a full test (and overhaul), the system is restored to an as-good-as-new condition. A partial-test policy is defined by the efficiency of the partial tests, and the number and distribution (periodic or non-periodic) of the partial tests in the full test time interval. Non-approximate equations are introduced for probability of failure on demand (PFD) assessment of a Moo N architecture (i.e. k-out-of- n: G) systems subject to partial and full tests. Partial tests may occur at different time instants (periodic or not) until the full test. The time-dependent, average, and maximum system unavailability (PFD(t), PFDavg, and PFDmax) are investigated, and the impact of the partial test distribution on average and maximum system unavailability are analysed, according to system architecture, component failure rates, and partial test efficiency.

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