Event Of Component Failure Research Articles

An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks

Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.

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.

Temporal Verification of Relay-Based Railway Traffic Control Systems Using the Integrated Model of Distributed Systems

Relay-based traffic control systems are still used in railway control systems. Their correctness is most often verified by manual analysis, which does not guarantee correctness in all conditions. Passenger safety, control reliability, and failure-free operation of all components require formal proof of the control system’s correctness. Formal evidence allows certification of control systems, ensuring that safety will be maintained in correct conditions and the in event of failure. The operational safety of systems in the event of component failure cannot be manually checked practically in the event of various types of damage to one component, pairs of components, etc. In the article, we describe the methodology of automated system verification using the IMDS (integrated model of distributed systems) temporal formalism and the Dedan tool. The novelty of the presented verification methodology lays in graphical design of the circuit elements, automated verification liberating the designer from using temporal logic, checking partial properties related to fragments of the circuit, and fair verification preventing the discovering of false deadlocks. The article presents the verification of an exemplary relay traffic control system in the correct case, in the case of damage to elements, and the case of an incorrect sequence of signals from the environment. The verification results are shown in the form of sequence diagrams leading to the correct/incorrect final state.

Efficient failure detection and consensus at extreme-scale systems

<span>Distributed systems and extreme-scale systems are ubiquitous in recent years and have seen throughout academia organizations, business, home, and government sectors. Peer-to-peer (P2P) technology is a typical distributed system model that is gaining popularity for delivering computing resources and services. Distributed systems try to increase its availability in the event of frequent component failures and functioning the system in such scenario is notoriously difficult. In order to identify component failures in the system and achieve global agreement (consensus) among failed components, this paper implemented an efficient failure detection and consensus algorithm based on fail-stop type process failures. The proposed algorithm is fault-tolerant to process failures occurring before and during the execution of the algorithm. The proposed algorithm works with the epidemic gossip protocol, which is a randomly generated paradigm of computation and communication that is both fault-tolerant and scalable. A simulation of an extreme-scale information dissemination process shows that global agreement can be achieved. A P2P simulator, PeerSim, is used in the paper to implement and test the proposed algorithm. The proposed algorithm results exhibited high scalability and at the same time detected all the process failures. The status of all the processes is maintained in a Boolean matrix.</span>

Microgrid resilience: a holistic and context-aware resilience metric

Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole. Boosting the resilience of distribution systems is another major benefit of microgrids. This is because they can also serve as a backup power source when the utility grid operations are interrupted due to either high-probability low-impact events like a component failure or low-probability high-impact events - be it a natural disaster or a planned cyberattack. However, the degree to which any particular system can defend, adapt, and restore normal operation depends on various factors including the type and severity of events to which a microgrid is subjected. These factors, in turn, are dependent on the geographical location of the deployed microgrid as well as the cyber risk profile of the site where the microgrid is operating. Therefore, in this work, we attempt to capture this multi-dimensional interplay of various factors in quantifying the ability of the microgrid to be resilient in these varying aspects. This paper, thus, proposes a customized site-specific quantification of the resilience strength for the individual microgrid capability to absorb, restore, and adapt to the changing circumstances for sustaining the critical load when a low-probability high-impact event occurs - termed as - context-aware resilience metric. We also present a case study to illustrate the key elements of our integrated analytical approach.

Risk-based bridge component importance measures under seismic loads

This article formulates risk-based component importance measures (RCIMs) to identify critical components for a bridge subjected to earthquakes. RCIM, unlike traditional reliability-based importance measures, is well suited for complex systems like bridges as it uses a flexible system failure definition using bridge level risk consequences or performance objectives. Contrasting traditional notions of risk and reliability related to structural system failures, the RCIMs embrace a broader definition of risk which includes consequences of system failure to society and the environment. System failure is defined as exceedance of a user-defined threshold of system risk (e.g. based on cost, emissions, embodied energy, or other metrics). Our definition of system failure using global performance metrics helps relating component to system reliability explicitly and offers an alternative perspective to aggregate joint failure events of bridge components into relevant damage states. RCIM combines information about the reliability of a component and its contribution to the system performance and hence the importance measures developed offer risk mitigation indicators for decision makers as to which components may require upgrade to achieve a given performance objective. The proposed RCIMs generalize existing importance measures through analytical exploration of the entire space of system configurations with correlated component failures, while also considering multiple risk-based criteria beyond reliability. These RCIMs, demonstrated through a seismic bridge system case study, further show that as the bridge components age and the hazard intensity varies, the relative contribution of the components to system risk also shifts.

A Four-Step Safety Integrity Level Analysis of Numerous Subsea Control System Components

Abstract In this paper, a four-step safety integrity level (SIL) analysis is developed to analyze numerous components of the subsea control system based on the OREDA database. For this purpose, initially, a failure mode classification table is provided, aiming to identify the number of dangerous as well as the prevalent failure modes. Then, several parameters including component inventory, installation inventory, equipment units, failure events, and total time in service (per hour) are calculated. Also, failure rates are evaluated and the software checking is provided as the final step of the proposed framework. The results show that the dangerous failure modes occur in about 80 cases, and the leakage in the closed position is the failure mode occurring more than the other ones. Moreover, calculations indicate that the process isolation valve of the subsea X-mass tree has the largest values in all the referred parameters. Moreover, hydraulic coupling of the choke module and HC leak sensor are the two components with the highest value of failure rates.

Performance of Male Condoms When Used With and Without a Silicone Placebo Vaginal Ring—A Randomized, Crossover Trial

Background:The dapivirine vaginal ring reduced the risk of HIV infection by approximately 30% in Phase III trials. To ensure higher levels of protection against HIV and sexually transmitted infections, women should be counseled to use condoms when using the vaginal ring. This article evaluates the compatibility of male condoms with a placebo vaginal ring.Methods:This was a 2-period crossover, randomized, noninferiority trial. Couples in 2 sites in the United States were randomized to male condom use, with and without a placebo silicone vaginal ring, and asked to use 4 male condoms in each period. The primary noninferiority end points were total clinical failure and their component failure events (clinical breakage or slippage). Frequencies and percentages were calculated for each failure mode and differences in performance of the 2 periods using the male condom without the ring as reference. Noninferiority was defined using a 3% margin at the 5% significance level. Safety and acceptability were also assessed.Results:Seventy couples were enrolled, and 68 completed the trial with a total of 275 male condoms used in each period. Total condom clinical failure rates were 2.2% and 4.0% in the presence and absence of the vaginal ring, respectively, with a difference of −1.9% (95% confidence interval: −5.3% to 1.5%), thereby demonstrating noninferiority when used with the ring. There was no difference in safety between the 2 periods.Discussion:Concurrent use of the placebo silicone vaginal ring had no significant effect on male condom functionality or safety outcomes.

Construction and quantification of a fire probabilistic safety assessment model using support system initiating event fault trees

This study investigated the effect of the fire-induced initiating event fault trees (IE FTs) related to the support systems of a reference plant, Hanul 3,4, on the risk quantification results by comparing the results of two (2) different kinds of fire probabilistic safety assessment (PSA) models: one based on the FT approach (with IE FTs) and the other based on the conventional approach (without IE FTs). The results show that the FT modeling of fire-induced IEs decreases fire-induced core damage frequencies (CDFs) if the IE FTs involve relatively many AND logic gates, because the fire-induced component failure events, the probabilities of which are less than '1′(TRUE), constitute the fire-induced IE FTs. The results also show that the CDFs increase if the fire-induced IE FTs involve relatively many OR logic gates. This is because a rare event approximation leads to overestimation of the real fire-induced risks.

Stochastic Model Checking for Predicting Component Failures and Service Availability

When a component fails in a critical communications service, how urgent is a repair? If we repair within 1 hour, 2 hours, or $n$ hours, how does this affect the likelihood of service failure? Can a formal model support assessing the impact, prioritisation, and scheduling of repairs in the event of component failures, and forecasting of maintenance costs? These are some of the questions posed to us by a large organisation and here we report on our experience of developing a stochastic framework based on a discrete space model and temporal logic to answer them. We define and explore both standard steady-state and transient temporal logic properties concerning the likelihood of service failure within certain time bounds, forecasting maintenance costs, and we introduce a new concept of envelopes of behaviour that quantify the effect of the status of lower level components on service availability. The resulting model is highly parameterised and user interaction for experimentation is supported by a lightweight, web-based interface.

Effects of Adjacent Spans and Correlated Failure Events on System-Level Hurricane Reliability of Power Distribution Lines

Past performance of distribution networks against hurricanes has highlighted the high vulnerability of these systems in electric power networks and the need for their reliability assessment for risk-informed decision-making purposes. A network level reliability analysis would require the estimation of reliability of line segments between key switches in the network. Former reliability studies, however, are based on a number of limiting assumptions. These include assuming independent mechanistic behavior of spans in a line segment during strong winds and statistical independence of failure events of components in a line. Due to correlations in wind loadings on the poles, environmental conditions that affect the decay rate of the poles, and structural properties of the poles, failures of poles are expected to be correlated events. This paper investigates the effects of these factors on the hurricane reliability of power distribution lines. Couplings in the wind performance of adjacent spans are incorporated here properly through an equivalent boundary model. Moreover, a multiscale matrix-based method is adopted to efficiently assess system reliability of distribution lines considering correlations in the failure events of line components. Numerical results indicate that couplings and statistical correlations have a considerable impact on the reliability of distribution systems.

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.

Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

The advent of ever more complex systems in extensive areas of industries hampers efficient and accurate analysis of reliability and effective reliability-based decision making. Such difficulties may arise from the intricate formulation of system failure events, statistical dependence between component failure events, and the convoluted quantification of underlying probabilities of basic events. So-called k-out-of-N systems, which survive or succeed when at least k components are available among the total of N components, give rise to a high level of complexity. This type of systems are commonly introduced to secure a proper level of redundancy in operating engineering systems, but the intricate definition of the system events may elude the system reliability analysis. It is noted that such k-out-of-N systems are often tested and corrected over a certain period of time before their official usage or release in order to assure the target reliability of the system. For the purpose of reliability prognosis based on the data collected from the test period, reliability growth models (RGMs) have been widely used in software and hardware engineering. However, RGMs have been applied mostly to individual components, not at the system level. Furthermore, in complex systems such as k-out-of-N system, it is challenging to relate the reliability growth of components with that of the system. To address this need, in this paper, the matrix-based system reliability (MSR) method is extended to k-out-of-N systems by modifying the formulations of event and probability vectors. The proposed methods can incorporate statistical dependence between component failures for both homogeneous and non-homogeneous k-out-of-N systems, and can compute measures related to parameter sensitivity and relative importance of components. The reliability growths of components represented by RGMs are incorporated into the proposed system reliability method, so that the trend of system reliability growth can be effortlessly evaluated and predicted. Two numerical examples are introduced in this paper to demonstrate the proposed method and its applications: (1) hypothetical systems each consisting of series, parallel and k-out-of-N subsystems, and (2) a simplified high speed train system modeled by multiple k-out-of-N subsystems. Two types of RGMs, i.e. non-homogeneous Poisson process (NHPP) and Duane models are employed in these examples.

Guest Editorial Power Grid Resilience

Recent extreme weather events have caused significant damage to power system infrastructure and resulted in unpredicted load interruptions from a few hours to several weeks. Despite the low probability of occurrence, the high impact of these events necessitates the study of power system resilience where effective models and algorithms are required to predict the event and likelihood of component failures, respond by utilizing new technologies, and recover by timely outage management and restoration processes. This issue aims at addressing the challenges and opportunities in deployment of smart grid technologies to achieve a resilient power system which would predict, respond, and quickly recover from extreme weather events and accordingly guarantee minimum customer interruption. Through a careful peer review process, 18 papers representing diverse topics on power grid resilience are included in this special section. Based on the various aspects of contributions, these papers are categorized and summarized in the following.

An Optimization Procedure to Evaluate the Service Performances in High-Speed Railway Lines Under Fault Conditions

The paper deals with train traffic operation in degraded conditions due to failure events of transportation systems components. An optimization algorithm, implemented in a computer procedure, able to minimize delays in case of scheduled constant train headways is first illustrated. The procedure can be applied for simulating and optimizing railway traffic two-way operation along the failed sections of a line and for taking into account, optionally, the presence of interconnections with other lines. Results of a case study involving a high-speed railway line are finally summarized.

Time-Dependent Risk Assessment of Bridges Based on Cumulative-Time Failure Probability

AbstractThis paper presents an approach for the time-dependent risk assessment of bridges based on cumulative-time failure probability of bridge members. The cumulative-time failure probabilities of girders under traffic load and pier columns under scour are computed. Different scenarios of lane closure are identified and expressed in terms of failure events of bridge components. Two extreme correlation cases among the failure modes of bridge components are considered. After estimating the occurrence probabilities and consequences associated with these scenarios, the time-dependent risks are assessed. Considering the entire history of loadings and structural deteriorating processes by using the cumulative-time approach provides a more realistic basis for risk assessment of bridges than the approach based on annual failure probabilities.

A Closed-Form Technique for the Reliability and Risk Assessment of Wind Turbine Systems

This paper proposes a closed-form method to evaluate wind turbine system reliability and associated failure consequences. Monte Carlo simulation, a widely used approach for system reliability assessment, usually requires large numbers of computational experiments, while existing analytical methods are limited to simple system event configurations with a focus on average values of reliability metrics. By analyzing a wind turbine system and its components in a combinatorial yet computationally efficient form, the proposed approach provides an entire probability distribution of system failure that contains all possible configurations of component failure and survival events. The approach is also capable of handling unique component attributes such as downtime and repair cost needed for risk estimations, and enables sensitivity analysis for quantifying the criticality of individual components to wind turbine system reliability. Applications of the technique are illustrated by assessing the reliability of a 12-subassembly turbine system. In addition, component downtimes and repair costs of components are embedded in the formulation to compute expected annual wind turbine unavailability and repair cost probabilities, and component importance metrics useful for maintenance planning and research prioritization. Furthermore, this paper introduces a recursive solution to closed-form method and applies this to a 45-component turbine system. The proposed approach proves to be computationally efficient and yields vital reliability information that could be readily used by wind farm stakeholders for decision making and risk management.

ACTIVE NEGATIVE INDUCTOR BASED ON MAGNETIC FLUX

An active circuit is described that exhibits the equivalent reactance of a negative inductor. In contrast with previous techniques based on negative impedance converters, the negative inductance is produced by generating an opposing time-varying magnetic ∞ux. The new circuit also enables the inductance to be electronically varied between negative and positive values, can enhance the Q, and defaults to a conventional positive inductor in the event of active component failure. The performance of a prototype with an inductance of i0:344mH and operating over the frequency range 1{40kHz is described.

FIRE PROPAGATION EQUATION FOR THE EXPLICIT IDENTIFICATION OF FIRE SCENARIOS IN A FIRE PSA

When performing fire PSA in a nuclear power plant, an event mapping method, using an internal event PSA model, is widely used to reduce the resources used by fire PSA model development. Feasible initiating events and component failure events due to fire are identified to transform the fault tree (FT) for an internal event PSA into one for a fire PSA using the event mapping method. A surrogate event or damage term method is used to condition the FT of the internal PSA. The surrogate event or the damage term plays the role of flagging whether the system/component in a fire compartment is damaged or not, depending on the fire being initiated from a specified compartment. These methods usually require explicit states of all compartments to be modeled in a fire area. Fire event scenarios, when using explicit identification, such as surrogate or damage terms, have two problems: (1) there is no consideration of multiple fire propagation beyond a single propagation to an adjacent compartment, and (2) there is no consideration of simultaneous fire propagations in which an initiating fire event is propagated to multiple paths simultaneously. The present paper suggests a fire propagation equation to identify all possible fire event scenarios for an explicitly treated fire event scenario in the fire PSA. Also, a method for separating fire events was developed to make all fire events a set of mutually exclusive events, which can facilitate arithmetic summation in fire risk quantification. A simple example is given to confirm the applicability of the present method for a <TEX>$2{\times}3$</TEX> rectangular fire area. Also, a feasible asymptotic approach is discussed to reduce the computational burden for fire risk quantification.

Real-Time Power Management of Integrated Power Systems in All Electric Ships Leveraging Multi Time Scale Property

All-electric ships (AES), enabled by integrated power systems (IPS), have been pursued for both commercial and military applications to meet the increasing ship-board power demand and environmental sustainability initiatives. They necessitate real-time power management (PM) for dynamic reconfiguration to support system critical operations in the event of dynamic load change or IPS component failures. The nonlinear, large scale trajectory optimization problem associated with IPS, along with the non-analytical nature of IPS model, makes many existing methods inadequate in meeting the real-time requirements. In this paper, we develop a methodology that exploits time scale separation, a characteristic associated with IPS dynamics, to achieve real-time optimization. In parallel, a dynamic model of the IPS with gas turbine and fuel cell as power plants is developed that captures the relevant dynamics but is simple enough for real-time optimization. The tradeoffs between the computational efficiency and optimization accuracy are analyzed. The optimization results for IPS PM on a real-time simulator are reported, which illustrate the real-time feasibility of the proposed optimization strategy.