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In recent years, renewable resources, especially large‐scale wind farms, are increasingly used to generate clean electricity in modern power systems. The penetration level of these power plants is significant, and due to the variation in the wind speed resulting in the variation in the generated power, different aspects of power systems, especially reliability, may be affected that must be studied. Reliability evaluation of power system considering large‐scale wind farms is dependent on two factors: the failure rates of composed components including turbine, generator, electrical converters, transformer, and cable and the variation in the generated power arising from variation in the wind speed. In this paper, dependency of different component failure rates on the wind speed variation is considered, and using the Monte Carlo simulation, reliability evaluation of power systems containing large‐scale wind farms is performed. A wind farm equipped with the permanent magnet synchronous generator is considered, and the failure rate of composed components considering wind speed variation is determined. Based on the wind speed‐dependent failure rate associated with the components of energy conversion system, the important reliability indices of Roy Billinton test system including loss of load expectation and expected energy not supplied are calculated.

Introduction To evaluate the effect of repeating test failures using an automated, non‐sequencing based non‐invasive prenatal testing test on a general‐risk population in Finland. Material and Methods A total of 545 samples from women who represent the average‐risk population in Oulu, Finland were analyzed with Vanadis® non‐invasive prenatal testing. Repeat testing of test failures was performed using a second sample. Results before and after repeat testing were compared with the reference outcome, as determined by clinical examination of neonates. Results There were eight test failures after first‐pass analysis, representing 1.5% of samples (95% CI 0.6%‐2.9%). Seven out of eight failures could be resolved by analysis of a second sample, thereby reducing the test failure rate from 1.5% to 0.2% (95% CI 0.0%‐1.0%). Conclusions Repeating test failures with a second plasma sample could significantly reduce the effective failure rate, thereby providing a way to effectively minimize test failures and further improving clinical utility and test performance.

Publicly available wind turbine failure rate and downtime studies are scarce in the scientific literature. Recent surveys in the field reveal that only 20 studies of wind farms based in Europe, Asia and the U.S. have been published, but there are no studies analysing wind farms located in Spain. With Spain holding the second place in terms of installed capacity in Europe, and the fifth worldwide, the importance of the Spanish wind power sector is evident. The present study comprehensively analyses the Spanish WT fleet in terms of yearly installed capacity and number of machines installed per technology and capacity ranges. The current fleet age ranges are also analysed. Under this scenario, the present work aims to contribute to the literature with a new failure rate and downtime study, where 75 WTs from six wind farms located in Spain are analysed over a period of 11 years. The most critical assemblies are identified per WT technology type and in general for the whole data set. Finally, the failure rate and downtime results obtained for the Spanish survey are compared against a selection of similar previously published studies.

Qualitative methods hold an important place in drug testing, filling central needs in screening and analyses, among others, linked to per se legislation. Nevertheless, the bioanalytical method validation guidelines do not discuss this type of method or describe method validation procedures ill-adapted to qualitative methods. The output of qualitative methods are typically categorical, binary results, such as presence/absence or above cut-off/below cut-off. As the goal of any method validation is to demonstrate fitness for use under production conditions, qualitative validation guidelines should evaluate performance based on discrete, binary results instead of the continuous measurements obtained from the instrument (e.g. area). A tentative validation guideline for threshold qualitative methods was developed by in silico modelling of measurements and derived binary results. This preliminary guideline was applied to a liquid chromatography-tandem mass spectrometry method for 40 analytes, each with a defined threshold concentration. Validation parameters calculated from the analysis of 30 samples spiked above and below the threshold concentration (false negative rate, false positive rate, selectivity rate, sensitivity rate and reliability rate) showed a surprisingly high failure rate. Overall, 13 out of the 40 analytes were not considered validated. A subsequent examination found that this was attributable to an appreciable shift in the standard deviation of the area ratio on a day-to-day basis, a previously undescribed and unaccounted-for behaviour in the qualitative threshold method validation literature. Consequently, the developed guideline was modified and used to validate a qualitative threshold method, based on the binary results for performance evaluation and incorporating measurement uncertainty.

In this paper, the life span of hydro and nuclear energy generations and the relationship between hydro and nuclear energy generations, environmental pollution, and economic growth were investigated for Japan covering the period of 1960–2018 by employing the Bathtub‐Weibull curve and Markov switching‐vector error correcting (MSVEC) method, respectively. According to the Bathtub‐Weibull curve analysis, a rising failure rate for nuclear energy was found, indicating that the life of nuclear energy has expired, but a decreasing failure rate for hydroelectric energy has been detected. Then two different MSVEC models were used. The MSVEC method, unlike traditional approaches, determines the relationship between variables under different regimes. The results of MSVEC methods indicate three important points. First, regime‐dependent asymmetry and regime changes are crucial for policy recommendations. Second, the shocks to hydropower and nuclear energy generations cause temporary deviations from the long‐run growth path in both regimes. Lastly, the increase in hydropower generation leads to a decrease in environmental pollution and an increase in GDP, and an increase in nuclear power generation increases pollution and growth in both regimes.

Cox regression models are routinely fitted to examine the association between time-dependent markers and a failure time when analyzing data from clinical registries. Typically, the marker values are measured periodically at clinic visits with the recorded value carried forward until the next assessment. We examine the asymptotic behavior of estimators from Cox regression models under this observation and data handling scheme when the true relationship is based on a Cox model using the current value of the marker. Specifically, we explore the impact of the marker process dynamics, the clinic visit intensity, and the marginal failure rate on the limiting value of the estimator of the marker effect from the Cox model. We also illustrate how a joint multistate model that accommodates intermittent observation of the time-varying marker can be formulated. Simulation studies demonstrate that the finite sample performance of the naive estimator aligns with the asymptotic results and shows good performance of the estimators from the joint model. We apply both methods to data from a study of bone markers and their effect on the development of skeletal complications in metastatic cancer.

Most pricing and revenue management models have at their core an optimization problem; one needs to determine the optimal price or quantity to maximize a profit or revenue function. To ensure tractability, conditions that assure the objective function has a unique solution are enormously helpful. So far, several technical assumptions have been proposed for the continuous case, but comparatively little attention has been given to the discrete counterpart despite its prevalence in practice. Thus, this study aims to develop new technical assumptions, built upon relevant economic concepts, to guarantee the tractability of revenue management models in discrete settings. In particular, we present two sufficient conditions for the revenue function to be concave, in terms of quantity or price and propose a condition for the revenue function to be unimodal, called discrete increasing generalized failure rate (IGFR). Our definition has an appropriate economic interpretation and offers comparable properties to those of the continuous version. Finally, we show the discrete IGFR property holds for several discrete distributions.

Reliability is critical to the design, operation, maintenance, and performance assessment and improvement of wind turbines (WTs). This paper systematically reviews publicly available reliability data for both onshore and offshore WTs and investigates the impacts of reliability on the cost of energy. WT failure rates and downtimes, broken down by subassembly, are collated from 18 publicly available databases including over 18 000 WTs, corresponding to over 90 000 turbine‐years. The data are classified based on the types of data collected (failure rate and stop rate) and by onshore and offshore populations. A comprehensive analysis is performed to investigate WT subassembly reliability data variations, identify critical subassemblies, compare onshore and offshore WT reliability, and understand possible sources of uncertainty. Large variations in both failure rates and downtimes are observed, and the skew in failure rate distribution implies that large databases with low failure rates, despite their diverse populations, are less uncertain than more targeted surveys, which are easily skewed by WT type failures. A model is presented to evaluate the levelised cost of energy as a function of WT failure rates and downtimes. A numerical study proves a strong and nonlinear relationship between WT reliability and operation and maintenance expenditure as well as annual energy production. Together with the cost analysis model, the findings can help WT operators identify the optimal degree of reliability improvement to minimise the levelised cost of energy.

We consider a company that schedules the tasks of its projects to maximize their expected net present value (ENPV) when tasks may fail. The failure of any task terminates the project immediately. We show that for projects with certain decreasing failure rates, the ENPV optimization problem can be solved using a linear program. The main focus of our work is on how constant task failure rates contribute to decreasing project risk as tasks are completed. Under constant task failure rate, earlier completion of a task improves its probability of success and the risk profile of the project. However, it may also accelerate costs, which worsen discounted cash flow. We show the equivalence of cash flow discount rate and failure rate. Further, if task failures are independent, then the failure rates are additive. We develop a model that (a) recognizes the reduction in project risk when a task is completed, (b) implements this risk reduction into the ENPV calculation, and (c) permits optimization of the ENPV through sequencing and timing decisions for the tasks. We design an algorithm to solve the problem optimally. This enables us to validate the contributions of our work using two computational studies. The first study demonstrates a significant increase in maximum project ENPV from improved project scheduling. The second study demonstrates a significant increase in total project portfolio value as a result of better informed project selection. Our work motivates companies to develop more precise information about the failure risks of their project tasks.

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.

Objectives/hypothesis The objectives of the study were to present an institutional experience with device failures and cochlear reimplantation rates over a 30-year period and to perform a detailed literature review. Study design Retrospective institutional experience and literature review. Methods A review of cochlear implant failures over a period of 30 years, between January 1988 and March 2017, at a single institution was conducted. Cochlear implant failures were calculated based on manufacturer, type of failure, and overall failure rate. Survival analysis was performed using Kaplan-Meier curves. An electronic search of the PubMed, Web of Science, and EMBASE databases revealed 24 articles on the topic of cochlear device failure. Data on reimplantation and device failure rates were extracted from this literature review and analyzed. Results A total of 804 cochlear implantations were reviewed from three manufacturers. The institutional reimplantation rate was 2.9% compared to the pooled rate of 6.0% calculated from the literature review. Medical failures accounted for 0.5% of the overall failures, device failures accounted for 1.6%, and inconclusive failures account for 0.7%. Survival analysis revealed a significant difference among manufacturers. An improved device failure rate was noted in the adult population (0.8%) as compared to the pediatric population (2.8%). Conclusions This 30-year review represents one of the longest series in the literature examining reimplantation, device failure, and medical failure rates. Cochlear implant survival varied by manufacturer and was significantly better in adult compared to pediatric patients. Level of evidence NA Laryngoscope, 130:782-789, 2020.

Predicting equipment failure is important because it could improve availability and cut down the operating budget. Previous literature has attempted to model failure rate with bathtub‐formed function, Weibull distribution, Bayesian network, or analytic hierarchy process. But these models perform well with a sufficient amount of data and could not incorporate the two salient characteristics: imbalanced category and sharing structure. Hierarchical model has the advantage of partial pooling. The proposed model is based on Bayesian hierarchical B‐spline. Time series of the failure rate of 99 Republic of Korea Naval ships are modeled hierarchically, where each layer corresponds to ship engine, engine type, and engine archetype. As a result of the analysis, the suggested model predicted the failure rate of an entire lifetime accurately in multiple situational conditions, such as prior knowledge of the engine.

Redox flow batteries are an interesting energy storage technology because they allow separate scaling of power and capacity. For their utilization on large scale, it is crucial to ensure reliable operation. Failure modes of elements of the system have been evaluated, both, regarding failure rate and severity of the different failures. As the main failure mode directly linked to a specific component of the redox flow technology, degradation of the membrane due to oxidation by vanadium ions has been identified. However, it is demonstrated that reliability is not solely determined by the specific electrochemistry of the technology. A huge share of the overall failure rate is due to mechanical components such as pumps, valves, and sealing. Based on the findings it can be recommended to design the systems with a certain redundancy regarding cells and pumps but avoid excessive redundancy. This is crucial not only because of high CAPEX of redundant systems, but also because of the increased complexity with more valves and connections required for integrating redundant units.

We consider Grenander-type estimators for a monotone function (Formula presented.), obtained as the slope of a concave (convex) estimate of the primitive of λ. Our main result is a central limit theorem for the Hellinger loss, which applies to estimation of a probability density, a regression function or a failure rate. In the case of density estimation, the limiting variance of the Hellinger loss turns out to be independent of λ.

During reliability analysis, analysts often encounter multiple repairable units operating in different environments for various applications such as marine, power generation, and propulsion. Thus, a reliability approach that accounts for varying operating conditions is invaluable to ensure system availability. Therefore, fault probability described by some characteristic parameters and their accurate estimation has been a vital task for understanding system's behavior. The time between failures is utilized to estimate Weibull parameters that define the system. The application of gas turbine is presented as a case study. Five different cases are discussed based on distinct operating conditions and faults. The results obtained demonstrate the effectiveness of the proposed method for assessing operational reliability. The three-parameter Weibull distribution was found to best fit the failure data with the root mean square error between 0.0369 and 0.0688 for maximum likelihood estimation and 0.04184 and 0.0733 for the least square method. Based on these results, it is deduced that the system under consideration is at the end of its operational life. Furthermore, it is observed that an increase in maintenance interval leads to a decline in meantime to failure, which is indicative of the need to select maintenance interval wisely. Findings from this study helps to improve the understanding of gas turbine behavior based on reliability and survival analysis. © 2019 American Institute of Chemical Engineers

Modelling the failure process of a repairable system is vitally important in many industrial sectors such as the offshore industry and the transport industry, in which properly maintaining assets is needed. Among the various models, the geometric process (GP) has been widely applied, mainly in the reliability and maintenance engineering, since its introduction. A book on the GP was published in 2007 and included the GP-related research by that year. However, since then, much research has been conducted, which creates a necessity to review the existing publications relating to the GP, its various extensions and applications since 2007. This paper serves for this purpose.

Summary In space‐based automatic identification system, message collision is inevitable. To separate collision messages in multichannel space‐based automatic identification system, an improved leas...

In this study, we propose two technical assumptions to ensure the unimodality of the objective functions in two classes of price and quantity decision problems with one procurement opportunity under supply random yield and deterministic demand in a price‐setting environment. The first class of problems involves a decentralized supply chain/assembly system under different configurations, and the second class focuses on a single firm's price and quantity decisions under different contracts, payment schemes and supplier portfolios. We provide appealing economic interpretations and easy‐to‐verify sufficient conditions for our proposed technical assumptions. We show that both assumptions are preserved under truncation and positive scale, and satisfied by most commonly used continuous yield distributions. Moreover, similar to the role that the increasing generalized failure rate (IGFR) property plays in analyzing operations problems with demand uncertainty, our Assumption 1 plays a fundamental role in regulating the behaviors of the objective functions for both classes of random yield problems. Assumption 2 is more general than both Assumption 1 and the IGFR property and is used to analyze the second class of problems. Finally, we discuss the difference between random yield and random demand problems, and explain the rationale for the need of different technical assumptions.

A coherent system that consists of n independent components and equipped with r cold standby components is considered. A generalized mixture representation for the survival function of such a system is obtained, and it is used to examine reliability properties of the system. In particular, the effect of adding r standby components to a given set of original components is measured by computing mean time to failure of the system. The limiting behavior of the failure rate of the system is also examined using the mixture representation. The results are illustrated for a bridge system. A case study that is concerned with an oil pipeline system is also presented.

Even after accumulating a portfolio of investment assets, retirees still need to know how much of their wealth they can spend each year without risking running out of money before they die. Traditionally, financial advisers use extensive simulation to predict a “failure rate†(i.e. the probability of a retiree running out of money before they die). Unfortunately, the failure rate approach is flawed because strategies with the same failure rate may not be comparable overall. For example, different withdrawal strategies may have the same failure rate but wind up leaving significantly different amounts of wealth behind. Instead of failure rate, this author recommends using the maximum withdrawal rate, or MWR, approach that predetermines a desired bequest (which could be zero or some positive sum), thus enabling an apples†to†apples comparison of retirement strategies. In addition to permitting direct comparisons of retirement strategies, the MWR provides a way to assess how likely a retiree is to sustain a target sequence of inflation†adjusted withdrawals. This is the main advantage of the MWR; it is a single tool that can be used to explore two of the most important issues in retirement analysis. The MWR as presented here is essentially a measure of the best a retiree could have done had he known the future returns of his portfolio. Obviously, one cannot be sure of future rates of return ahead of time, so the MWR does not eliminate all retirement risk. That said, its adoption would be an improvement upon current practice.

The IEC 61511 standard requires a verification calculation that a proposed design for a safety instrumented function (SIF) achieves the desired safety integrity level (SIL). The evaluation of the SIL of a new or existing safety instrumented system requires detailed calculations based on the failure rates of the device and the planned maintenance/testing cycle for the system. The failure rates of the devices are often taken from standard failure rate tabulations of equipment. The maintenance and testing plans are developed based on plant experience. All of the data used in the SIL calculations are uncertain. This article develops a general method for uncertainty analysis of the SIL calculations. The general method is based on the application of probability theory—variance contribution analysis—to the equations presented in ISA TR 84.00.02-2115. An example is worked to demonstrate the methodology. © 2017 American Institute of Chemical Engineers Process Saf Prog, 2017

Today, in reliability analysis, the most used distribution to describe the behavior of electronic products under voltage profiles is the Weibull distribution. Nevertheless, the Weibull distribution does not provide a good fit to lifetime datasets that exhibit bathtub-shaped or upside-down bathtub–shaped (unimodal) failure rates, which are often encountered in the reliability analysis of electronic devices. In this paper, a reliability model based on the beta-Weibull distribution and the inverse power law is proposed. This new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. To estimate the parameters of the proposed model, a Bayesian analysis is used. A case study based on the lifetime of a surface mounted electrolytic capacitor is presented, the results showed that the estimation of the proposed model differs from the inverse power law–Weibull and that it affects directly the mean time to failure, the failure rate, the behavior, and the performance of the capacitor under analysis.

Epidemiologic studies and disease prevention trials often seek to relate an exposure variable to a failure time that suffers from interval-censoring. When the failure rate is low and the time intervals are wide, a large cohort is often required so as to yield reliable precision on the exposure-failure-time relationship. However, large cohort studies with simple random sampling could be prohibitive for investigators with a limited budget, especially when the exposure variables are expensive to obtain. Alternative cost-effective sampling designs and inference procedures are therefore desirable. We propose an outcome-dependent sampling (ODS) design with interval-censored failure time data, where we enrich the observed sample by selectively including certain more informative failure subjects. We develop a novel sieve semiparametric maximum empirical likelihood approach for fitting the proportional hazards model to data from the proposed interval-censoring ODS design. This approach employs the empirical likelihood and sieve methods to deal with the infinite-dimensional nuisance parameters, which greatly reduces the dimensionality of the estimation problem and eases the computation difficulty. The consistency and asymptotic normality of the resulting regression parameter estimator are established. The results from our extensive simulation study show that the proposed design and method works well for practical situations and is more efficient than the alternative designs and competing approaches. An example from the Atherosclerosis Risk in Communities (ARIC) study is provided for illustration.

This article proposes a methodology for incorporating electrical component failure data into the human error assessment and reduction technique (HEART) for estimating human error probabilities (HEPs). The existing HEART method contains factors known as error-producing conditions (EPCs) that adjust a generic HEP to a more specific situation being assessed. The selection and proportioning of these EPCs are at the discretion of an assessor, and are therefore subject to the assessor's experience and potential bias. This dependence on expert opinion is prevalent in similar HEP assessment techniques used in numerous industrial areas. The proposed method incorporates factors based on observed trends in electrical component failures to produce a revised HEP that can trigger risk mitigation actions more effectively based on the presence of component categories or other hazardous conditions that have a history of failure due to human error. The data used for the additional factors are a result of an analysis of failures of electronic components experienced during system integration and testing at NASA Goddard Space Flight Center. The analysis includes the determination of root failure mechanisms and trend analysis. The major causes of these defects were attributed to electrostatic damage, electrical overstress, mechanical overstress, or thermal overstress. These factors representing user-induced defects are quantified and incorporated into specific hardware factors based on the system's electrical parts list. This proposed methodology is demonstrated with an example comparing the original HEART method and the proposed modified technique.

Summary State-of-the-art approaches to probabilistic assessment of seismic structural reliability are based on simulation of structural behavior via nonlinear dynamic analysis of computer models. Simulations are carried out considering samples of ground motions supposedly drawn from specific populations of signals virtually recorded at the site of interest. This serves to produce samples of structural response to evaluate the failure rate, which in turn allows to compute the failure risk (probability) in a time interval of interest. This procedure alone implies that uncertainty of estimation affects the probabilistic results. The latter is seldom quantified in risk analyses, although it may be relevant. This short paper discusses some basic issues and some simple statistical tools, which can aid the analyst towards the assessment of the impact of sample variability on fragility functions and the resulting seismic structural risk. On the statistical inference side, the addressed strategies are based on consolidated results such as the well-known delta method and on some resampling plans belonging to the bootstrap family. On the structural side, they rely on assumptions and methods typical in performance-based earthquake engineering applications. Copyright © 2017 John Wiley & Sons, Ltd.

Availability of a system is a crucial factor for planning and optimization. The concept is more challenging for modern systems such as robots and autonomous systems consisting of a complex configuration of components. In this paper, a reliability evaluation framework is developed for a system of binary state autonomous robots in an automated manufacturing environment. In this framework, the concepts in functional block diagram, table of truth, and sum of state are employed simultaneously to develop a binary state reliability model. Due to inefficacy of the method for larger number of components involved in complex systems, an extension of the Bernoulli trials is proposed. In an implementation study, the effectiveness and computational efficiency of the proposed method are illustrated. In addition, an analysis on the failure rate using the maximum likelihood estimation and confidence interval is reported. Copyright © 2017 John Wiley & Sons, Ltd.

We consider the classical problem of whether certain classes of lifetime distributions are preserved under the formation of coherent systems. Under the assumption of independent and identically distributed (i.i.d.) component lifetimes, we consider the NBUE (new better than used in expectation) and NWUE (new worse than used in expectation) classes. First, a necessary condition for a coherent system to preserve the NBUE class is given. Sufficient conditions are then obtained for systems satisfying this necessary condition. The sufficient conditions are satisfied for a collection of systems which includes all parallel systems, but the collection is shown to be strictly larger. We also prove that no coherent system preserves the NWUE class. As byproducts of our study, we obtain the following results for the case of i.i.d. component lifetimes: (a) the DFR (decreasing failure rate) class is preserved by no coherent systems other than series systems, and (b) the IMRL (increasing mean residual life) class is not preserved by any coherent systems. Generalizations to the case of dependent component lifetimes are briefly discussed.

Personalized cancer therapy requires clinical trials with smaller sample sizes compared to trials involving unselected populations that have not been divided into biomarker subgroups. The use of exponential survival modeling for survival endpoints has the potential of gaining 35% efficiency or saving 28% required sample size (Miller, 1983), making personalized therapy trials more feasible. However, the use of exponential survival has not been fully accepted in cancer research practice due to uncertainty about whether or not the exponential assumption holds. We propose a test for identifying violations of the exponential assumption using a reduced piecewise exponential approach. Compared with an alternative goodness-of-fit test, which suffers from inflation of type I error rate under various censoring mechanisms, the proposed test maintains the correct type I error rate. We conduct power analysis using simulated data based on different types of cancer survival distribution in the SEER registry database, and demonstrate the implementation of this approach in existing cancer clinical trials.

The evaluation of the safety integrity level (SIL) of a new or existing safety instrumented system (SIS) requires detailed calculations based on the failure rates of the device and the planned maintenance-testing cycle for the system. The failure rates of the devices are taken from standard failure rate tabulations of equipment. The maintenance and testing plans are developed based on plant experience. The quantitative evaluation determines the probability of failure on demand (PFD) for a demand mode SIS and yields the SIL of the SIS. All of the data used in the SIL calculations are uncertain. This article explores the impact of uncertainty on the PFD calculation for a SIS. The “70%” rule of thumb from IEC 61508 is compared to results obtained using probability theory such as variance contribution analysis (VCA). A proposed methodology for handling the uncertainty in the PFD calculations is presented based on the application of the VCA method. An example is worked to demonstrate the methodology. © 2016 American Institute of Chemical Engineers Process Saf Prog 35: 341–348, 2016

Gas transmission pipeline network is of great importance to any country using natural gases in its various technological processes. However, the usefulness cannot overshadow the threat posed to people and property by the grid failures. In order to quantify the reliability of the grid, se veral widely recognized pipeline incident databases have been established. However, each database contains data about pipelines operated in remote geographical regions with varying soil types, under different incident registration criterion. For a longer time period even in single database, there is variation of these incident registration criteria. Therefore, analysis of an entire sample without regard to the incident criteria change raises suspicions about the validity of resulting inferences. Authors move beyond the qualitative pipeline incident database comparison and provide a methodology for quantitative integration of all available statistical information to improve gas pipeline network reliability evaluation. We develop a new model called Criteria-dependent Poisson model, which takes into account various incident data collection criteria and extend it to the hierarchical (Bayesian) case when different databases with differing incident registration criteria can be joined in the same analysis. With the real data examples, we demonstrate the applicability of our method, which unfolds itself to be of great usefulness in reliability prediction. The Lithuanian pipeline network failure rate assessment shows the advantages of hierarchical structuring of Criteria-dependent Poisson model in small sample problems. Copyright © 2015 John Wiley & Sons, Ltd.

Present reliability models, which estimate the lifetime of electronic devices, work under the assumption that the voltage level must be constant when an Accelerated Life Testing is performed. Nevertheless, in a real operational environment, electronic devices are subjected to electrical variations present in the power lines; that means the voltage has a time-varying behavior, which breaks the assumption of reliability models. Thus, in this paper, a reliability model is presented, which describes the lifetime of electronic devices under time-varying voltage via a parametric function. The model is based on the Cumulative Damage Model with random failure rate and the modified Inverse Power Law. In order to estimate the parameters of the proposed model, the maximum likelihood method was employed. A case study based on the time-varying voltage induced by electrical harmonics when Alternate Current/Direct Current (AC/DC) transformer is connected to the power line is presented in this paper. Copyright © 2015 John Wiley & Sons, Ltd.

In the present work, starting from well-known methodologies, a new reliability allocation method [critical flow method (CFM)] has been proposed. We focused on the most important conventional methods and discussed their limitations in order to motivate the current research. The results show the main common problem of the most conventional reliability allocation methods: they are developed for complex systems with series configurations but not for series–parallel ones. The consequence is an increase of the required units' reliability (series configuration) in order to guarantee the reliability system target. Actually, the design and manufacturing of a subsystem with an extremely low failure rate would consume a considerable amount of economic resources. The proposed method can solve the shortcomings of the conventional methods with a new reliability approach useful to series–parallel configurations in order to obtain an important cost saving. The CFM has been applied to a liquid nitrogen cooling installation in a thermonuclear system, with many series–parallel configurations in order to guarantee the whole safety system. The proposed technique can be applied to working complex systems, and, in general, in the design phase of new installations. By comparing the CFM application results with real parameters, the new technique has been validated. The computational results clearly demonstrate the advantages of the proposed method. In particular, by applying the method to series–parallel configurations, it allocates failure rates higher than conventional methods, with a component cost reduction. Copyright © 2015 John Wiley & Sons, Ltd.

Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub-assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub-assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers.

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub-shaped failure rate data of newly marketed consumer electronics. The time-dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average-based and exponential smoothing-based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique. Copyright © 2015 John Wiley & Sons, Ltd.

Spinal fusion surgeries have a high failure rate for difficult-to-fuse patients. A piezoelectric spinal fusion implant was developed to overcome the issues with other adjunct therapies. Stacked generators were used to improve power generation at low electrical load resistances. The effects of the number of layers on average maximum power and the optimal electrical load resistance were characterized. The effects of mechanical preload, load frequency, and amplitude on maximum power and optimal electrical load resistance were also characterized. Increasing the number of layers from one to nine was found to lower the optimal electrical load resistance from 1.00 GΩ to 16.78 MΩ while maintaining maximum power generation. Mechanical preload did not have a significant effect on power output or optimal electrical load resistance. Increases in mechanical loading frequency increased average maximum power, while decreasing the optimal electrical load resistance. Increases in mechanical loading amplitude increased average maximum power output without affecting the optimal electrical load resistance. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 104B: 158–164, 2016.