Reliability Of Safety-critical Systems Research Articles

Reliability and Performance Measurement of Safety-Critical Systems Based on Petri Nets: A Case Study of Nuclear Power Plant

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

Assessment of an accidental hydrogen leak from a vehicle tank in a confined space

This study aims to characterize some safety aspects by examining the geometries of the infrastructure, currently used by societies, against the accumulation of hazardous hydrogen clouds during an accidental leak in areas with limited ventilation. Using ANSYS FLUENT as a modeling tool, the influence of garage roof shape; pyramidal and domed roof compared with the basic model (flat roof), for different leak times, on dispersion and stratification of hydrogen layers, is analyzed. As a result, the domed roof promotes to have a lower hydrogen concentration and presents two remarkable peaks of the Richardson number (Ri) with the highest value more than 2 × 105, which is three times higher than the flat roof. Besides, the influence of the leak time on the dynamic of the flow, concentration, and stratification process are observed: the mole fraction of hydrogen is more than 0.25 after 1 h of leak, whereas it is lower than 0.05 after 100 s. The volume flow and therefore the flammable volume increase. This study highlights the importance of geometrical and sizing parameters on the characteristics of hydrogen leaks and subsequently gives insights to establish performance standards for the availability and reliability of safety critical systems.

Modeling and Measuring Common Cause Failures in Measurement of Reliability of Nuclear Power Plant Systems

Much attention has been given to measure the reliability of safety critical systems (SCSs), assuming that components fail independently. However, in practice, dependent failures of components due to come common cause play a significant role in determining system reliabilities and hence can measure the risk more accurately. In this article, we propose a method to measure such dependent failures systematically, known as common cause failures (CCFs). We propose a framework to model and quantify CCF that contains two phases: screening analysis and detailed analysis. Screening analysis involves qualitative and quantitative screening; detailed analysis involves estimation of common cause basic event probabilities by developing a probability model. The proposed approach is applied on a case study of nuclear power plant (NPP). The result proves that risk is highly sensitive to the probability of a CCF.

Software reliability analysis for safety‐critical and control systems

AbstractThe transition from analog to digital safety‐critical instrumentation and control (I&C) systems has introduced new challenges for software experts to deliver increased software reliability. Since the 1970s, researchers are continuing to propose software reliability models for reliability estimation of software. However, these approaches rely on the failure history for the assessment of reliability. Due to insufficient failure data, these models fail to predict the reliability of safety critical systems. This paper utilizes the Bayesian update methodology and proposes a framework for the reliability assessment of the safety‐critical systems (SCSs). The proposed methodology is validated using experiments performed on real data of 12 safety‐critical control systems of nuclear power plants.

Software Failure Analysis using FMEA

The reliability of safety-critical systems is of concern, a failure of which would result ininjury, death, damage to the environment or financial loss. Such systems have evolved frombeing largely mechanical to computer driven. The approach to analysing failure ofmechanical or hardware architecture of the system is well established in literature andpractice. Although the hardware architecture could be adequately analysed for failureusing traditional safety analysis techniques, the manner in which the software architectureis to be analysed for failures is fuzzy. This creates additional concern to the reliability ofcontemporary safety-critical systems. This paper defines an approach to analysing softwarefailure at class diagram using one of traditional safety analysis techniques, failure modesand effects analysis (FMEA). It also demonstrates how to apply the approach to analysingsoftware failure.

An optimized technique for reliability analysis of safety‐critical systems: A case study of nuclear power plant

AbstractStochastic models are extensively used in quantifying the reliability of safety critical systems. These models use the state‐space model for reliability quantification. Markov chain is comprehensively used in describing a sequence of possible events of any system in which the probability of each event depends only on the state attained in the previous event. Markov chains are convenient to model the software system of the SCS with the help of Petri Nets, a directed bipartite graph widely used for the verification and validation of real‐time systems. However, the stochastic model suffers from the state‐space explosion problem. In this paper, we proposed a technique for reliability analysis of safety critical systems, excavating into the coherent optimization of Markov chain. The approach has been validated on 17 safety critical systems of nuclear power plants.

Simplification of inclusion–exclusion on intersections of unions with application to network systems reliability

Reliability of safety-critical systems is a paramount issue in system engineering because in most practical situations the reliability of a non series-parallel network system has to be calculated. Some methods for calculating reliability use the probability principle of inclusion–exclusion. When dealing with complex networks, this leads to very long mathematical expressions which are usually computationally very expensive to calculate. In this paper, we provide a new expression to simplify the probability principle of inclusion–exclusion formula for intersections of unions which appear when calculating reliability on non series-parallel network systems. This new expression exploits the presence of many repeated events and has many fewer terms, which significantly reduces the computational cost. We also show that the general form of the probability principle of inclusion–exclusion formula has a double exponential complexity, whereas the simplified form has only an exponential complexity with a linear exponent. Finally, we compare its computational efficiency against the sum of disjoint products method KDH88 for a simple artificial example and for a door management system, which is a safety-critical system in aircraft engineering.

An Incorporated Voting Strategy on Majority and Score- based Fuzzy Voting Algorithms for Safety-Critical Systems

Triple Modular Redundancy (TMR) is usually used to increase safety and the reliability of safety-critical systems where three identical segments are used in identical and the ultimate outcome is reached using voting techniques. Fault masking is one of the main techniques to improve the normal actions of a range of safety-critical techniques. Some commercial areas which implement such techniques include process control, transport, and atomic power place and army programs. Integrated Voter for majority and weighted-average used to provide for a fault masking capability in safety-critical systems [1]. The Majority voting gives a high level of safety and the weighted-average offers a good level of availability. If integrated these two gives a good level of safety in Majority voting not in integrated voting. Here propose a new voting algorithm for faulty masking taking the disadvantage of previous algorithm. In this Incorporating Majority voting and score based fuzzy voting schemes. Safety performance is evaluated by running proposed, Majority and score based fuzzy voting on a triple modular redundant (TMR) system for 10000 voting cycles in various error scenarios. Experimental results show that proposed fuzzy Voter is given a higher safety than other two voting algorithms.

Impact of Prior Loading on the Reliability of Safety Critical Systems

Probabilistic models are developed to determine changes in the survival probability particularly when prior loading contributes to changes in residual stress and fracture toughness. Experimental evidence for variations in toughness and residual stress is examined and then incorporated into probabilistic models. The structure’s reliability is consistently better after prior loading.