System Reliability Estimation Research Articles

Health assessment of the electrical contact-line connections in view of the operational traction load pattern of the electric rolling stock

Estimation of the traction energy system reliability and efficiency due to timely and immediate identification of heating sites of contact joints is the future-oriented technology for the condition oriented service and repair of the electrical equipment of energy providers. It is peculiar with the roughly alternating load of the electric current action during the day. This makes it difficult to identify the current state of the electrical connection. Thus, it becomes necessary to specify the dependence of changes in the assessment criteria of the electrical connection condition in operation.

Comparative Analysis of Reliability Indices and Hydraulic Measures for Water Distribution Network Performance Evaluation

The performance of water distribution networks (WDNs) can be quantified by several types of hydraulic measure. In design and operation of a WDN, sufficient consideration should be given to system performance, and it would be inefficient to separately consider individual characteristics of hydraulic measures. Instead, various reliability indices have been developed and utilized to evaluate the performance of WDNs; however, deciding which index to use according to a particular WDN situation has not been investigated in sufficient depth. In this regard, this study analyzes the correlation between representative reliability indices and hydraulic measures to propose the most adequate reliability index according to the desired system performance in various situations. Specifically, six hydraulic measures representing system performance were selected from the viewpoint of redundancy, robustness, and serviceability. In addition, nine indices for estimating system reliability were classified based on theoretical backgrounds such as hydraulic, topological, entropic, and mixed approaches. The correlations between the nine indices and six measures were analyzed using 17 sample hypothetical networks with different layouts, under three water supply scenarios, and the overall evaluation results for each reliability index are presented through multi-criteria decision analysis.

E- Bayesian Estimation of System Reliability (Series, Parallel) and Failure Rate Functions with Kumaraswamy Distribution based on Type II Censoring Data

In this paper, the failure rate function and the shape parameter for the kumaraswamy distribution and reliability function of a system with a number (m) of independent compounds associated with a system (serial, parallel) were estimated, by relying on observational data of the second type, knowing that the survival time of the compounds are independent. Based on the findings the graphical predictor of the failure rate and parameter - and the reliability function of the serial and parallel system is smaller than the Standard Bayesian estimator (MLE) in simulation and real data. Thus, a decreasing in AMPE with an increase in the sample size n and an increase in the size of the failure sample r as the physical prediction capabilities have a high efficiency. The using of the Bayesian prediction method to estimate the reliability of different production systems for other failure distributions such as the Burr family distributions and various other failure distributions. Based on the output he results are reasonably consistent with simulation and real data. The E-Bayesian method was used for estimating with three primary distribution functions for the above parameters and comparing them with the standard Bayesian method with a squared loss function and the maximum likelihood method where simulation experiments were employed to compare the estimation results and the results showed the advantage of the E-Bayesian method in estimating through comparison statistics (MAPE).

A novel opportunistic maintenance strategy for systems with dependent main and auxiliary components

Abstract The complexity of dependence between different types of components results in many challenges to estimate system reliability and to optimize maintenance plans. In this paper, we develop a reliability model to study the failure dependence of a system with a main component and several protective auxiliary components. Damage to the main component caused by random environmental shocks depends on the number of auxiliary components in operation. When the execution of inspection and maintenance actions during system operation is difficult, failures of the main component provide opportunities to inspect and replace the auxiliary components. We derive the system reliability using Laplace transforms and the matrix method. The optimization problem is solved by an enumeration method. A numerical example and sensitivity studies of cost parameters show how the evolution of the parameters influences the optimal maintenance strategy. The results show that a high replacement threshold of the auxiliary components is required when the replacement cost of the main component is high. Conversely, the threshold could be adjusted to a lower level when the replacement cost of the auxiliary components and the downtime cost increase.

An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

The mission reliability and success probability estimation of multistate systems under complex mission conditions are studied. The reliability and success probability of multistate phased mission systems (MS-PMS) is difficult to use analytic modeling and solving. An estimation approach for mission reliability and success probability based on Monte Carlo simulation is established. By introducing accelerated sampling methods such as forced transition and failure biasing, the sampling efficiency of small-probability events is improved while ensuring unbiasedness. The ship’s propulsion and power systems are used as applications, and the effectiveness of the method is verified by a numerical example. Under complex missions, such as missions with different mission time and their combinations, and phased-missions, the proposed method is superior in small-probability event sampling than the crude simulation method. The calculation example also studies the influence of mission factors or system reliability and maintainability factors on system availability and mission success probability, and analyzes the relationship between different mission types and system availability and success probability.

Conditional probability-based system reliability analysis for geotechnical problems

In geotechnical engineering, multiple failure modes exist, and any one can contribute to the failure of a system. System reliability is related to both the probability of failure of each failure mode and the correlations between them, which is obviously different from the reliability analysis of a single mode. This paper focuses on system reliability analysis for geotechnical engineering application, and proposes an efficient system reliability analysis method based on conditional probability, which is implemented in the ubiquitous Excel spreadsheet platform. By using the so-called common source random variables concept, the system reliability problem is converted to the calculation of a conditionally independent failure event. The procedure is illustrated for a circular rock tunnel with three failure modes, a tetrahedral rock wedge with four failure modes, and a slope with two and eight failure modes. In addition, comparisons are made between the proposed method with the bimodal bounds approach and the multivariate normal cumulative distribution function method. The study also illustrates the use of the second-order reliability method (SORM) to refine the reliability indices and improve the accuracy of the estimated system reliability in a case where limit state surfaces are not hyperplanes.

Reliability block diagram (RBD) and fault tree analysis (FTA) approaches for estimation of system reliability and availability – a case study

PurposeIn the present worldwide situation, the survival of a business is a major crucial aspect. The business cannot be succeeded unless it produces the anticipated production levels. Achievement of this can be possible only by maintaining the equipment into an adequate level. Load-Haul-Dumpers (LHDs), as the main workhorse and massive transporting machines, are highly utilized in underground mining operations. Despite the usage of LHDs, these are prone to the uneven and unexpected occurrence of potential failures. These are causes to minimize the production and productivity of capital intensive equipment. To get a good profitability index, it is very necessary to have the required levels of equipment reliability and availability. Estimation of reliabilities and availabilities play a critical role in the performance evaluation of equipment.Design/methodology/approachBy keeping the significance of the present research work in view in this research paper one of the well appropriate techniques such as fault tree analysis (FTA) was utilized to assess the reliability of the LHD system based on the function flow diagram. Best fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K-S) test. Parametric estimation of theoretical probability distributions was done by utilizing the maximum likelihood estimation (MLE). Failure rate of each LHD system has computed based on the best fit results from “Isograph Reliability Workbench 13.0”. Reliability configuration of each LHD system has modeled using reliability block diagram (RBD), as well as the FTA.FindingsIndependent and identical distribution (IID) assumption of data sets was validated through statistic U-test (Chi Squared test). On the basis of test results, the data sets are in accordance with IID assumption. Therefore renewal process approach has been utilized for further investigation. Allocations of best fit distribution of data sets were made by the utilization ofK-S test. Parametric estimation of theoretical probability distributions was made by utilizing maximum likelihood estimation (MLE) method. Reliability of each individual subsystem has been computed according to the best fit distribution. The deductive method called RBD was utilized to investigate the given system reliability by analyzing with graphical representations of logic system and observed highest percentage of reliability as 69.44% (LH29). FTA has been utilized to investigate the availability percentage of a system and observed highest percentage value as 79.51% (LH29). This technique also helps to identify the most critical parts/cut sets by using Fussell-Vesely (F-V) importance measure.Research limitations/implicationsAs the reliability analysis is one of the complex techniques, it requires strategic decision-making knowledge for the selection of methodology to be used. As the present case study was from a public sector company, operating under financial constraints the conclusions/findings may not be universally applicable.Originality/valueThe present study throws light on this equipment that need a tailored maintenance schedules, partly due to the peculiar mining conditions, under which they operate. This analysis provides the information on several aspects such as present working condition of the machines, occurrence of various potential failure modes, influence of failure modes on its performance and reliable life aspects etc. Also, these investigations asses the forecasting of necessary managerial practices or control measures like possible design modifications and replacement actions of components to ensure the required levels of availability and utilization of the equipment. Both qualitative and quantitative analysis of FTA has been performed to determine the minimal/most influencing cut sets of the system and to estimate overall system availability within the work environment. Based on the computed results reasons for performance drop of each machine was identified and suitable recommendations were suggested to improve the performance of capital intensive systems.

Multicomponent stress-strength system reliability estimation for generalized exponential-poison distribution

This study deals with (s -out of- k) Multicomponent Stress (Y) and Strength (X) System Reliability Estimation. Both stress and strength assumed to have Generalized Exponential-Poisson Distribution with common and known scale parameters (θ and λ). The aim here is to estimate the unknown shape parameters (α and β) for X and Y respectively using two methods of estimation ML and Bayes analysis by one prior with five loss functions. Then estimate Reliability using the same methods and compared the results by Mean square error criteria from simulation study to find the best performance of the estimators. the results show that the best estimator for R(s,k) is Bayes estimator under Quadratic loss function using Gamma prior function, followed by GD, GW, GP, MLE and GS estimators, respectively.

Reliability Equations for Cloud Storage Systems with Proactive Fault Tolerance

As cloud storage systems increase in scale, hard drive failures are becoming more frequent, which raises reliability issues. In addition to traditional reactive fault tolerance, proactive fault tolerance is used to improve a system's reliability. However, there are few studies which analyze the reliability of proactive cloud storage systems, and they typically assume an exponential distribution for drive failures. This paper presents closed-form equations for estimating the number of data-loss events in proactive cloud storage systems using RAID-5, RAID-6, 2-way replication, and 3-way replication mechanisms, within a given time period. The equations model the impact of proactive fault tolerance, operational failures, failure restorations, latent block defects, and drive scrubbing on the systems reliability, and use time-based Weibull distributions to represent processes (instead of homogeneous Poisson processes). We also design a Monte-Carlo simulation method to simulate the running of proactive cloud storage systems. The proposed equations closely match time-consuming Monte-Carlo simulations, using parameters obtained from the analysis of field data. These equations allow designers to efficiently estimate system reliability under varying parameters, facilitating cloud storage system design.

System reliability computation and estimation based on a cumulative damage–threshold–shock model

PurposeThis article considers a reliability model where the failure is due to cumulative damage exceeding a threshold level. The concept that the threshold level of cumulative damage at each arrival of shock can change based on whether the magnitude of each shock exceeds its defined threshold level is considered to compute the system reliability.Design/methodology/approachThe stochastic process approach is used to obtain the cumulative damage based on Poisson arrival of shocks. The general expression for reliability is obtained using the conditional probability over each arrival of shock. The method of maximum likelihood estimation is used to obtain the estimators of the parameters and system reliability. A sensitivity analysis is performed to measure the effect of the parameter representing the rate of arrival of shock.FindingsThe maximum likelihood estimates of the reliability approach the actual reliability for increasing sample size. A sensitivity analysis study on the parameter representing the rate of arrival of shock shows that as the values of parameter increase (decrease), the reliability value decreases (increases).Originality/valueObtained a new expression for the cumulative damage–shock model and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability. The sensitivity analysis also genuinely supports our findings.

English

English

On Bayesian Estimation of System Reliability in Stress – Strength Model Based on Generalized Inverse Rayleigh Distribution

The parameter and system reliability in stress-strength model are estimated in this paper when the system contains several parallel components that have strengths subjects to common stress in case when the stress and strengths follow Generalized Inverse Rayleigh distribution by using different Bayesian estimation methods. Monte Carlo simulation introduced to compare among the proposal methods based on the Mean squared Error criteria.

System Reliability Analysis With Autocorrelated Kriging Predictions

AbstractWhen limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples.

The harm that judges do – misunderstanding computer evidence: Mr Castleton’s story

The reliability of computer systems, and the reliability of documents produced by computers, is important. There is a widely held perception that computers are fundamentally reliable. Further, it is commonly assumed that most computer errors are readily detectable or otherwise the result of user ‘input’ error. Since 1997 the courts have applied the presumption of the proper functioning of machines to computers. This article considered the case of Lee Castleton to illustrate that this presumption can have a devastating effect when applied.
 Index words: software, electronic evidence, computer evidence, presumption of reliability

Multi-objective unit commitment optimization with ultra-low emissions under stochastic and fuzzy uncertainties

Low cost, high reliability and low pollution are prime targets when performing current unit commitment optimization. As an extension of previous works, this study establishes a multi-objective unit commitment model which takes into account all of the above targets. The main content includes: First, the pricing support for thermal units with ultra-low emissions is involved when analyzing the operation cost of generation systems, which accords with the current policy of power markets. Second, a conditional Value-at-Risk-based measurement is formed to estimate system reliability considering the stochastic and fuzzy uncertainties existed in future load, renewable generation and equipment failures, which is sensitive to tail risks and provides easy-to-adjust conservativeness against worst-case scenarios. Third, to deal with the proposed model, a practical approach is applied to develop a multi-objective particle swarm optimization algorithm, which improves the Pareto fronts obtained by existing methods. The effectiveness of this research is exemplified by two case studies, which demonstrate that the model finds appropriate pricing support for the reformed units, and the proposed reliability measurement is able to realize a number of trade-offs between cost effective and solution robustness, thus providing decision support for system operators. Finally, the comparisons on performance metrics such as spacing and hyper-volume also justify the superiority of the algorithm.

System Reliability Estimation of Divert Attitude Control System of a Launch Vehicle using Bayesian Networks

Divert attitude and control system (DACS) is a one-shot system and provides attitude correction and translation of the Launch vehicle. DACS consists of many flight critical sub systems which are arranged in a series configuration. The traditional Reliability block diagram and Fault tree diagram methods are unsuitable for reliability modelling, when considering uncertainty among the components and system. Bayesian network is the natural choice to model dependencies among the components and system. DACS being one shot system, it is very expensive and time consuming to test more number of systems during the design and development. Hence the data is drawn from component level, subsystem level and expert opinion is used for reliability estimation. In this paper, Bayesian network modelling of DAC system was carried out for estimating the reliability using multi-level data. An algorithm is developed for computation of Conditional probabilities in Bayesian network. Posterior probability distribution of components is calculated using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of DAC system.

Reliability Model and Sensitivity Analysis for General Electronic Systems with Failure Types based on Non-identical Correlated Components

Reliability is the most important qualitative properties of products and industrial electronic systems. Today, industrial electronic systems need to produce products with maximum reliability. In every industry, when a system fails, it becomes harmful in various aspects such as economic, human and political; therefore, accurate estimation of system reliability is very important. Previous methods to calculate system reliability assumed that a large number of components failures are statistically independent. Considering such a hypothesis makes it possible to calculate probability and mathematical computation, but it does not provide perfect system reliability. This paper presents a simple and new technique for reliability analysis by considering unequal reliability and non-identical distribution of correlated components for k-out-of-n and coherent systems. The efficiency of our proposed method is demonstrated by computing the reliability of Bridge system. The results show that the function of existing components correlation has a major impact, and that ignoring it has a significant effect on system safety.

A hybrid Gaussian process model for system reliability analysis

This paper proposes a new system reliability analysis method based on a hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models. The proposed method is named hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of an engineered system according to their interdependencies: MGP models are constructed over the groups of highly interdependent components and UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to learn and quantify the pairwise dependencies between the components involving both linear and complex nonlinear dependency patterns. An initial hybrid Gaussian process (HGP) model comprising both UGP and MGP models is first constructed with a set of near-random samples and these UGP and MGP models are then updated with new samples that are sequentially identified based on a new acquisition function named adaptive multivariate probability of improvement. Results of three mathematical and two real-world engineering case studies suggest that the proposed HGP-SRA method can achieve better accuracy and efficiency in system reliability estimation than several benchmark surrogate-based methods.

Liquid Propulsion System Reliability Estimation using Computational Bayesian Approach with Multi-level Data

Liquid propulsion system (LPS) is a highly reliable and complex system that is used for the military and space applications. It consists of many flight critical components arranged in series configuration. Reliability is the most critical parameter for this system, even one subsystem failure leads to total failure of flight vehicle. Determining the achieved reliability of a LPS is a unique challenge for designer of these systems. The system reliability needs to be estimated with limited number of tests due to the destructive nature of tests, time and cost constraints. In this paper, reliability of LPS was estimated with subsystem test data using computational Bayesian approach. Component level, subsystem level and system level data are considered and a framework is created by combing all information. The reliability of the LPS was calculated using Markov Chain Monte Carlo (MCMC) simulation which has avoided numerical integration. Results are compared with Lindstorm Madden method and Bayesian hybrid method. Computational Bayesian approach can give reasonably better reliability estimate with limited test data.

Reliability estimation in a multicomponent stress–strength based on unit-Gompertz distribution

PurposeThe purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter.Design/methodology/approachThe reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples.FindingsBased on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known.Originality/valueMost of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.