Frequency Of System Failure Research Articles

Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.

Estimation of uncertainty in computation of importance factors and probability of undesirable events for safety-critical multi-unit systems, based on Binary Decisions Diagrams

This paper addresses current needs of the Multi-Unit Probabilistic Safety Assessment (MUPSA) methodology, which consists of a series of eight essential steps for conducting Probabilistic Safety Assessment (PSA) in the context of a multi-unit Nuclear Power Plant (NPP), co-located on a single site. The focus of this study revolves around one of the steps of the MUPSA methodology, which includes the examination of the importance factors and uncertainty analysis. Emphasizing the role of this step within MUPSA, the paper highlights the hypothesis and estimations made, along with the determination of system importance factors and failure frequency. To illustrate the practical application of these concepts, the paper presents three illustrative examples. The first example involves a 3-terminal reliability network, while the second example features an illustrative calculation applied to a system designed for potential use in a real NPP, compared to a Monte-Carlo simulation. Moreover, the second example involves the evaluation of a medium voltage distribution system (MVDS) designed to meet the auxiliary supply requirements of a nuclear power plant. This analysis is performed using a binary decision diagram (BDD), and the results derived from the BDD evaluation are then verified with those obtained through a Monte Carlo simulation for a comprehensive comparative analysis. Finally, an uncertainty analysis is achieved using a BDD reliability model, using the results of fault trees developed by National Company SN Nuclearelectrica S.A. (SNN), Department of Nuclear Safety, in the context of the Coordinated Research Project (CRP- I31031).

Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays

AbstractThis prospective study focuses on analyzing the reliability characteristics of multi‐unit systems with standby provisioning, accounting for failures, degradation, random delays, and probabilistic imperfections. The investigation sheds light on how these unreliable attributes hinder the performance and availability of machining systems. Given the frequent occurrence of these negative attributes within machining systems, their impact on production flow, performance, and resource utilization is significant. Moreover, these unfavorable attributes hinder the adoption of advanced technologies that rely on the continuous availability of machining systems. Thorough research on operational traits serves as a foundation for developing solutions to enhance machining system efficiency and elucidates the underlying causes of machining system failures throughout their performance lifecycle. The reliability analysis employs a state‐of‐the‐art queue‐theoretic approach. In order to model the stochastic behavior of the investigated machine repair problem in a systematic manner, we consider various statistically independent failure modes, including active/standby unit failure, degraded failure, switching failure, and common‐cause failure. The unreliable characteristics of machining systems are further compounded by factors such as imperfect fault coverage, reboot delay, and imperfect repair, necessitating a comprehensive examination to strategically implement preventive, corrective, and predictive measures. The seamless operation of multi‐unit machining systems is essential for the successful integration of advanced technologies such as cloud computing, industry 4.0, and IoT. Failures, delays, degradation, and imperfections within machining systems have detrimental effects on their efficiency and availability, demanding in‐depth investigation. To facilitate numerical experimentation and sensitivity analysis of the reliability aspects of the proposed machining system, we develop performance indices such as system reliability, mean‐time‐to‐failure, and failure frequency. These metrics provide valuable insights for decision‐makers seeking to implement measures that ensure uninterrupted availability of the machining system.

Dynamic and dependent tree theory (D2T2): A framework for the analysis of fault trees with dependent basic events

• A fault tree framework is provided which overcomes limitations in conventional analysis methods. • Component failure events can have any distribution of failure or repair times. • Component dependencies can be accommodated. • Complex maintenance processes for components and sub-systems can be incorporated. • The algorithm identifies independent sub-problems which are solved accurately and efficiently. Fault tree analysis remains the most commonly employed method, particularly in the safety critical industries, to predict the probability or frequency of system failures. Whilst it has its origins back in the 1960s, the assumptions employed in the majority of commercial fault tree analysis codes have not changed significantly since this time and restrict the ability of the method to represent features of the design, operation and maintenance of modern industrial systems. The inability to include general dependencies between the basic events, the requirement for invariant failure and repair rates, and the inability to account for complex maintenance strategies are major limitations. This paper proposes a new fault tree analysis framework which can overcome these restrictions. Whilst retaining the fault tree structure to express the causality of the system failure, the internal calculation method is updated by exploiting features of the Binary Decision Diagram, Stochastic Petri Net and Markov methods. The key elements of the D 2 T 2 algorithm are described in detail and the framework demonstrated through application to a case study example of a pressure vessel cooling system.

Comparative Analysis of the Failure Rates of Shearer and Plow Systems—A Case Study

Mechanised plow and shearer systems are widely applied in underground mines all over the world. Both systems are used in the exploitation of hard coal deposited in the form of seams of various thickness. The selection of the appropriate complex depends on the mining and geological conditions and the thickness of the seam. However, with regard to thin and medium seams, these complexes are competitive solutions. Mines usually use either shearer or plow systems. Both have certain advantages and disadvantages resulting from their design and method of operation, which have been demonstrated and presented in many publications. However, in terms of their failure rate comparison, there are no relevant research and analysis results. Only selective studies of individual machines can be found. The article is concerned with the failure frequency of longwalls equipped with plow and shearer systems in the LW Bogdanka coal mine. The analysis covers a period of 13 months of the mine’s operation, during which 2589 failures were recorded. All failures were taken into account, irrespective of their type or cause. The analysis was conducted for all longwalls exploited in this period, i.e., five plow and five shearer systems working in six different sections. In the analysed period, these longwalls worked for a total of 1484 days. It should be emphasised that all the complexes worked in one mine, thanks to which the data are comparable. The analysis is unique material regarding the failure rate of machines. Both solutions were analysed independently and subjected to a detailed comparison. A comprehensive analysis revealed that the failure rate of longwalls equipped with plow systems is noticeably higher than that of shearer ones. The main purpose of the article was to conduct a comparative analysis of the failure rate of machines in shearer and plow complexes operating in the same conditions. The analysis results contradict the previous opinion on the failure frequency of plow and shearer systems. The final conclusion has been very well-argued and is supported by hard data. The comparison of both techniques in terms of their failure rate is new knowledge and can be treated as an argument when choosing an appropriate longwall complex.

Research on the frequency of battery energy storage system failures

AbstractAn introduction to the current state of failure frequency research for battery energy storage systems (BESS) is provided. The article discusses the many failure modes of BESS and how the reliability data are scarce and the design changes are fast‐paced. Current public resources available for overcycle datasets and battery datasets are summarized. Efforts are under way to better communicate failure rate research among stake holders. This is the second article in a six‐part series.

Climate change and infrastructure risk: Indoor heat exposure during a concurrent heat wave and blackout event in Phoenix, Arizona

Concurrent with a rapid rise in temperatures within US cities, the frequency of regional electric grid system failures is also rising in recent decades, resulting in a growing number of blackouts during periods of extreme heat. As mechanical air conditioning is a primary adaptive technology for managing rising temperatures in cities, we examine in this paper the impact of a prolonged blackout on heat exposure in residential structures during heat wave conditions, when air conditioning is most critical to human health. Our approach combines a regional climate modeling system with a building energy model to simulate how a concurrent heat wave and grid failure event impacts residential building-interior temperatures across Phoenix. Our results find a substantial increase in heat exposure across residential buildings in response to the loss of electrical power and mechanical cooling systems, with such an event potentially exposing more than one million residents to hazardous levels of heat. We further find the installation of cool roofing to measurably lower the risk of extreme heat exposure for residents of single-story structures.

Societal risk assessment of terminal and oil refinery unit

This paper presents the societal risk assessment of oil refinery in one of the oil companies in East Java. The oil company operates-a terminal for loading and unloading of variety petrochemical products. The products are categorized as flammable which could be hazardous. To prevent unlikely event of hazards such as fire, risk assessment is needed to know the level of risk and propose any mitigation if the risk is unacceptable. The risk assessment is done for all the processes that occurs such as loading and unloading of products and the chemical process in the oil refinery. The system is categorized into 8 nodes based on their function. Firstly, Hazard and Operability (HAZOP) is carried out using BS IEC 61882 standard to identify the deviation of the system, the causes and how to prevent it. The frequency analysis of failure of component is done by using Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) which based on the DNV failure guidance and Oil and Gas Producers (OGP) data as input. In order to know the level of consequence, fire modelling is carried out to simulate how the jet fire and pool fire could impact into the social around the oil refinery in form of heat intensity. The fire modelling for the pool fire and jet fire results the pool radius and flame length respectively, based on the heat intensity of 4 kW/m2, 12.5 kW / m2, and 37.5 kW / m2. Risk is represented using F-N curve with UK-HSE standard. There is unacceptable risk that incurred for jet fire in pipeline and its components from the feedstock tank into pre-cut column, scenario 200 mm hole diameter. Mitigation is done using LOPA to bring the risk to the acceptable region by adding a gas detector which has changed the frequency of system failure from 1.44E-03 to 2.88E-06.

Improved reliability assessment of backup battery storage integrated with power supply system in a building

This paper presents the improved analysis of reliability for battery storage used in power system. The reliability assessment of this paper includes the evaluation of reliability of the system components, battery module and power electronic components. Battery storage is considered as one of energy storage and energy source that commonly used in power system. The evaluation of the reliability of power systems utilising with the storage batteries is performed by using the Markov chain process. The computation of the reliability is conducted by referring to the generated reliability block begins from power supply system. Every part of the system is evaluated regarding two specific states that are in normal or failure mode. By using the Markov method, the system unavailability and failure frequency can be computed.<span style="font-size: 9pt; font-family: 'Times New Roman', serif;" lang="EN-GB">This paper presents the improved analysis of reliability for battery storage used in power system. The reliability assessment of this paper includes the evaluation of reliability of the system components, battery module and power electronic components. Battery storage is considered as one of energy storage and energy source that commonly used in power system. The evaluation of the reliability of power systems utilising with the storage batteries is performed by using the Markov chain process. The computation of the reliability is conducted by referring to the generated reliability block begins from power supply system. Every part of the system is evaluated regarding two specific states that are in normal or failure mode. By using the Markov method, the system unavailability and failure frequency can be computed.</span>

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the system’s components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression. Previous work noted the paradox of robustness. However, no general theory for the evolutionary dynamics of system robustness and component decay has been developed. This article takes a first step by linking engineering control theory with the genetic theory of evolutionary dynamics. Control theory emphasizes error-correcting feedback as the single greatest principle in robust system design. Linking control theory to evolution leads to a theory for the evolutionary dynamics of error-correcting feedback, a unifying approach for the evolutionary analysis of robust systems. This article shows how increasingly robust systems accumulate more genetic variability and greater stochasticity of expression in their components. The theory predicts different levels of variability between different regulatory control architectures and different levels of variability between different components within a particular regulatory control system. The theory also shows that increasing robustness reduces the frequency of system failures associated with disease and, simultaneously, causes a strong increase in the heritability of disease. Thus, robust error correction in biological regulatory control may partly explain the puzzlingly high heritability of disease and, more generally, the surprisingly high heritability of fitness.

Performance analysis of an unreliable M/G/1 retrial queue with two-way communication

Efficient use of call center operators through technological innovations more often come at the expense of added operation management issues. In this paper, the stationary characteristics of an $M/G/1$ retrial queue is investigated where the single server, subject to active failures, primarily attends incoming calls and directs outgoing calls only when idle. The incoming calls arriving at the server follow a Poisson arrival process, while outgoing calls are made in an exponentially distributed time. On finding the server unavailable (either busy or temporarily broken down), incoming calls intrinsically join the virtual orbit from which they re-attempt for service at exponentially distributed time intervals. The system stability condition along with probability generating functions for the joint queue length distribution of the number of calls in the orbit and the state of the server are derived and evaluated numerically in the context of mean system size, server availability, failure frequency and orbit waiting time.

Probability and Frequency Derivation Using Dynamic Fault Trees

Dynamic Fault Tree (DFT) is suitable to capture functional and dynamic dependencies among events leading to system failure. There exist several approaches for analyzing DFTs, each of them suffers from drawbacks that limit its practical use. This paper presents a comprehensive methodology based on the structure function determination. It consists in performing both qualitative and quantitative analysis to establish failure sequences and to calculate the likelihood (probability and frequency) of dynamic systems failure. Failure sequences are extracted from the minimal canonical form of the top event and allow getting disjunctive failure sequences. From the quantitative point of view, the probability and frequency of any DFT top event are developed to make the proposed model able to quantify the failure probability and frequency of dynamic systems and the frequency of accident scenarios. The proposed procedure is applied on two benchmark examples: the first one is a safety‐related system while the second one is an accident scenario with dependent protection layers. In order to check the validity of the proposed method, the derived numerical results are compared with those obtained from Markov Chains models. © 2018 American Institute of Chemical Engineers Process Saf Prog 37: 535–552, 2018

Combined nuclear safety-security risk analysis methodology development and demonstration through a case study

Destruction of critical nuclear infrastructure would have a debilitating effect on national public health, safety, national economy and security. For this reason, analysts perform safety risk analyses on the performance of the nuclear system to quantify and understand the nature of unwanted events. Since the world has gone through many changes after the terrorist attacks of 9/11, nuclear security risk analysis also became a necessity. To date, the safety and security risk analyses have been done separately without a combined evaluation. Study results are presented for three types of risk analyses for a pure security initiating event, pure safety initiating event, and a combined analysis of safety-security risk for either a security or safety initiating event. The pure security risk analysis uses adversary sequence diagram and pathway analysis to calculate the initiating security event frequency of a successful adversary attack. The pure safety analysis represented a series of natural (random) safety system component failure events for which a safety system failure frequency was calculated using SAPHIRE probabilistic risk analysis code. On the other hand, the combined safety-security analysis considered a security initiating event followed by safety system failure or vice versa. The main outcome of the comparative study of three different types of risk analyses is that pure safety risk evaluation without considering the possibility of a simultaneous security attack would underestimate the risk value. Failure frequency due to a security event should be combined with the safety system failure analysis for a meaningful risk analysis and the Estimate of Adversary Sequence Interruption (EASI) model can be employed for this purpose. The usefulness of a combined safety-security risk analysis is demonstrated through a case study for the spent fuel storage pool facility.

Application of Quality Management Tools for Evaluating the Failure Frequency of Cutter-Loader and Plough Mining Systems

Abstract Failure frequency in the mining process, with a focus on the mining machine, has been presented and illustrated by the example of two coal-mines. Two mining systems have been subjected to analysis: a cutter-loader and a plough system. In order to reduce costs generated by failures, maintenance teams should regularly make sure that the machines are used and operated in a rational and effective way. Such activities will allow downtimes to be reduced, and, in consequence, will increase the effectiveness of a mining plant. The evaluation of mining machines’ failure frequency contained in this study has been based on one of the traditional quality management tools - the Pareto chart.

Determination of hydrate inhibitor injection rate for flowlines based on Monte Carlo method

The present study proposes a new methodology that estimates the acceptable injection rate of a hydrate inhibitor considering the random failure and subsequent maintenance of the topside system followed by the cooldown of the flowlines and risers where hydrate can potentially form. The proposed methodology consists of four steps: system description, topside system simulation, subsea system simulation, and construction of the exceedance curve for the inhibitor injection. As the system is defined in the first step, Monte Carlo simulation (MCS) is performed in the topside system simulation step to predict the system failure frequency and subsequent downtime that leads to the exceedance curve of the downtime. With the exceedance curve of the downtime, the longest downtime, equivalently the longest cooldown time, τLC, is estimated. In the subsea system simulation step, the cooldown path of the inventory is obtained by multiphase simulation and τTD, the time that the inventory touch down the hydrate formation region is defined. The last step combines the MCS with multiphase simulation to yield the frequency of the required MEG injection to avoid the hydrate formation. The exceedance curve provides risk-based information for determining the required MEG injection rate based on risk acceptance criteria, which may vary with different points of view. Four case studies are performed for several installations that have different extent of process configurations and insulation.

DATA ANALYSIS AND PROCESSING FOR THE SYSTEM RELIABILITY NEURAL NETWORK BASED ON EXPERT JUDGMENT

The article presents a data analysis and processing for tuning artificial neural network (ANN) of the anthrop technical system reliability, based on the opinions of experts. In general, the system reliability parameters are functions of operands – physical values – like time to failure, time between failures, duration times of specific reliability or operational states, number of failures in a time interval (event frequencies). These values are easier to be determined by an expert – operator with long year experience – than probabilistic model parameters. It is suggested that they be used in elicitation, for example linguistic estimates of the shares of reliability system elements in the system failure frequency. The numerical – linguistic elicitation of these opinions was carried out, which turned out to be uncorrelated and not suitable for tuning the network. Data processing method was used with the appropriate adopted analytic hierarchy process (AHP) geometric scale and matrix approximation method evaluations (logarithmic least squares method). Correlation analyses were performed for received input and output data of network and error of data processing method was determined. The results are shown in the example of elicitation and data correlation analyses for tuning the reliability neural network of the ship propulsion system.

Property-based rainwater drainage design and the impacts of climate change

Property-based rainwater drainage provision comprises a number of components broadly categorised as roof, surface or underground drainage. These systems are relied upon to prevent water ingress to the building and to avoid localised ponding or flooding. Recognition of the inherently unsteady response of the integrated network, in part driven by a particular rainfall event but also due to the transient nature of the fluid flows therein, is important in allowing an understanding of system performance under both current and future climate conditions. Codes and standards suggest that roof drainage systems, conventional or siphonic, are designed using a single rainfall intensity figure, representative of the most intense part of a longer storm, whereas runoff to surface and underground drainage is typically based on a single-peaked rainfall profile. Together with the difference in event duration typically adopted as part of the design process, this means that, unless a straightforward uplift factor is applied, then understanding the potential impacts of climate change on overall system performance can be difficult. Using numerical modelling techniques to analyse the performance of a case study site located in Edinburgh, this research identifies an appropriate common rainfall event from which a system-specific exceedance threshold is identified. Specification of this rainfall intensity facilitates an exploration of the impacts of climate change undertaken within the context of UKCP09 projections. Using gutter overtopping as an indicator of failure, this paper explores possible changes in the frequency of system under-capacity under varying future climate change scenarios. Practical application: Engineers are encouraged, in relevant codes and standards, to take account of the potential impacts of climate change when designing rainwater drainage systems for buildings. However, little guidance is offered therein on how to achieve this. This paper presents an example of UKCP09 climate change projections, specifically Weather Generator precipitation data, applied to a case study site that integrates roof, surface and underground drainage networks. Results illustrate the possible change in frequency of system failure, identified in this case as gutter overtopping.

New methods to determine the importance measures of initiating and enabling events in fault tree analysis

Components' importance measures play a very important role in system reliability analysis. They are used to identify the weakest parts of the system for design improvement, failure diagnosis and maintenance. This paper deals with the problem of determining the importance measures of basic events in case of unreliability analysis of binary coherent and non-coherent fault trees. This type of analysis is typical of catastrophic top events, characterised by unacceptable consequences. Since the unreliability of systems with repairable components cannot be exactly calculated via fault tree, the Expected Number of Failures – which is obtained by integrating the unconditional failure frequency – is considered as it represents a good upper bound. In these cases it is important to classify events as initiators or enablers since their roles in the system are different, their sequence of occurrence is different and consequently they must be treated differently. New equations based on system failure frequency are described in this paper for determining the exact importance measures of initiating and enabling events. Simple examples are provided to clarify the application of the proposed calculation methods. Compared with the exact methods available in the literature, those proposed in this paper are easier to apply by hand and are simpler to implement in a fault tree analyser.

Cost of ownership model for spare engines purchase for the Korean navy acquisition program

Major weapon system acquisition programmes often require high initial purchase cost which can be a burden for the procurement of a highly reliable system. In order to avoid the tendency of acquiring a less expensive weapon system with lower performance, a cost of ownership (COO) model can be applied to assess the lifetime cost of the weapon system. In many existing cost estimation models for weapon systems, the failure rate of the system is assumed to be constant and the functional relationship between the initial purchase cost and maintenance cost is not well defined. In this paper, we propose a revised COO model where random effects models are employed to accommodate the variations of the system failure frequency and repair time. It is expected that our model can contribute to the cost-effective procurement of spare engines for the Korean Navy acquisition programme within the limited national defence budget.

Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.