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Ensuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements. Design/methodology/approach: A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company. Findings: Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system's failure rate and increasing the system availability more effectively. Research limitations/implications: Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions. Originality/value: This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software. [ABSTRACT FROM AUTHOR]

Introduction. Insulation coordination requires accurate prediction of overvoltages at various points within a substation. Computer simulations of electromagnetic transients in real structures of substations become more precise due to the improvements of used models. Goal. This paper discusses in a first step the use of the modified ZnO arrester dynamic model alongside other substation equipment models, considering electrical phenomena like the corona model for reproducing the stresses that lightning can cause in an air-insulated substation and (method) then conducting a statistical approach based on the Monte Carlo method. The implemented MATLAB/ATP procedure estimates not only the substation Mean Time between Failures (MTBF), but also is used to select surge arresters or substation basic insulation levels (BILs). In this procedure ATP transients program is used to calculate lightning overvoltages and multicore environment for the calculations. Results. The obtained MTBF curves offer guidance for selecting appropriate insulation levels based on specific system requirements and conditions. The obtained results comply well with existing international insulation standards. This valuable approach significantly contributes to the field of lightning protection. References 31, tables 3, figures 10.

Introduction. Insulation coordination requires accurate prediction of overvoltages at various points within a substation. Computer simulations of electromagnetic transients in real structures of substations become more precise due to the improvements of used models. Goal. This paper discusses in a first step the use of the modified ZnO arrester dynamic model alongside other substation equipment models, considering electrical phenomena like the corona model for reproducing the stresses that lightning can cause in an air-insulated substation and (method) then conducting a statistical approach based on the Monte Carlo method. The implemented MATLAB/ATP procedure estimates not only the substation Mean Time between Failures (MTBF), but also is used to select surge arresters or substation basic insulation levels (BILs). In this procedure ATP transients program is used to calculate lightning overvoltages and multicore environment for the calculations. Results. The obtained MTBF curves offer guidance for selecting appropriate insulation levels based on specific system requirements and conditions. The obtained results comply well with existing international insulation standards. This valuable approach significantly contributes to the field of lightning protection. References 31, tables 3, figures 10. [ABSTRACT FROM AUTHOR]

A high gain DC-DC converter is the crucial part in renewable energy systems (RES) and in electric vehicular systems. The reliability of those high-gain converters needs to be assessed for the long-term operation of renewable energy systems. This article presents the reliability analysis of non-isolated high gain interleaved DC-DC converter. The analysis primarily relies on calculating the mean time between failures (MTBF). Based on military handbook (MIL-HDBK-217) criteria, the reliability calculation is performed. Stress factors and predicted failure rate for each component of presented converter is evaluated and tabulated. Reliability evaluation is performed for 1.5 kW hardware prototype. Based on reliability evaluation results, a reliable converter with better operating life time has been introduced. [ABSTRACT FROM AUTHOR]

Solar energy is expected to overtake wind energy as the leading renewable energy source by 2050, and therefore, accurate lifetime and reliability predictions for solar photovoltaic (PV) installations are necessary. The microinverter is a pivotal component of stand-alone PV power systems. The reliability of the microinverter depends on the mission profile of the geographical location, such as solar irradiance, ambient temperature, and wind velocity. This study developed a comprehensive reliability map for a 200Wp monocrystalline panel and a 250W microinverter based on mission profile data collected from 198 locations across India. A novel mathematical model is developed and validated using Ansys Icepak to evaluate the junction temperatures of microinverter components. The results show that the junction temperature significantly affects the reliability of PV system. In the hot and dry climatic zone of Jaisalmer, Rajasthan, the maximum MOSFET junction temperature and Mean Time Between Failure (MTBF) obtained were 63.14°C, and 27.34 years, respectively. Furthermore, in the cold and dry climatic zone of Leh, Ladakh, the junction temperature dropped to 29.74°C, increasing MOSFET’s MTBF to 49.8 years. These results prove that the reliability depends on the mission profile, and it should not be ignored in evaluating the reliability of solar PV systems. [ABSTRACT FROM AUTHOR]

To improve estimating accuracy in the traditional mean rank or median rank estimation method, an improved mean rank is proposed in the correction principle of rank estimation function as the cumulative distribution function of samples by adjusting the applicable points of natural mean rank. Then a least squares estimation is performed by directly fitting the cumulative distribution function. Based on the hypothesis of Weibull distribution under small sample, the parameter estimations under different ranks are calculated by Monte Carlo simulation. The results indicate that the relative error on calculating scale parameter using the improved mean rank method for the Weibull distribution with different parameters is less than 9.5% under the condition of sample size not less than 4. Furthermore, the relative error on calculating mean time between failures using the improved mean rank method is less than 8.7%, while the relative errors using traditional methods are higher than 16%. From calculated results, proposed method can effectively improve the parameter estimation accuracy for Weibull distribution.

Electrical submersible pumps are widespread in the oil industry, in particular, for production of watered mixture in periodic pumping mode. Intermittent mode allows reducing energy costs due to application of high-rate equipment with higher efficiency and prompt change of well fluid withdrawal during change of well parameters without changing equipment. This method is also used when producing oil with high gas factor, with stops for gas phase passing and liquid accumulation. However, this method assumes the presence of peak force loads acting on the pump shaft and supports, as a result of which destruction of electric motor winding insulation, crushing of dowels and keyways of impellers, as well as accumulation of residual deformations of shafts and supports take place. In this relation in view of specific features of electrical submersible pumps installations operation, in particular, installations operating in a periodic pumping mode, the identification of factors influencing the operating time of pumping equipment in order to reduce failures is an actual task at the present moment. The accounting of parameters, determining the system operating time, allows preventing the failures in advance. Mean time between failures depends on many parameters of technological mode. The known methods of calculating mean time between failures are of qualitative character only and allow estimating at what moment in time or by what reason the failure will happen. Statistical distributions (exponential, normal, Weibull distributions etc.) on which base the most probable values of operating time depending on the value of the influencing parameter, or within a certain time are determined, cannot be used for evaluation of the effect of several parameters, which are typical for the presented system. The method based on the regression and statistical analysis of the sample of the wells, granted by one of the Western Siberia companies, is presented in the work for considering several parameters, which have the greatest influence on the operating time to failure, as well as the most influencing factors on the electrical submersible pumps installations life time in the periodic mode are determined.

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The reliability of software has a tremendous influence on the reliability of systems. Software dependability models are frequently utilized to statistically analyze the reliability of software. Numerous reliability models are based on the nonhomogeneous Poisson method (NHPP). In this respect, in the current study, a novel NHPP model established on the basis of the new power function distribution is suggested. The mathematical formulas for its reliability measurements were found and are visually illustrated. The parameters of the suggested model are assessed utilizing the weighted nonlinear least-squares, maximum-likelihood, and nonlinear least-squares estimation techniques. The model is subsequently verified using a variety of reliability datasets. Four separate criteria were used to assess and compare the estimating techniques. Additionally, the effectiveness of the novel model is assessed and evaluated with two foundation models both objectively and subjectively. The implementation results reveal that our novel model performed well in the failure data that we examined. [ABSTRACT FROM AUTHOR]

In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decisionmaking for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner's unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production's safety. [ABSTRACT FROM AUTHOR]

The reliability of software has a tremendous influence on the reliability of systems. Software dependability models are frequently utilized to statistically analyze the reliability of software. Numerous reliability models are based on the nonhomogeneous Poisson method (NHPP). In this respect, in the current study, a novel NHPP model established on the basis of the new power function distribution is suggested. The mathematical formulas for its reliability measurements were found and are visually illustrated. The parameters of the suggested model are assessed utilizing the weighted nonlinear least-squares, maximum-likelihood, and nonlinear least-squares estimation techniques. The model is subsequently verified using a variety of reliability datasets. Four separate criteria were used to assess and compare the estimating techniques. Additionally, the effectiveness of the novel model is assessed and evaluated with two foundation models both objectively and subjectively. The implementation results reveal that our novel model performed well in the failure data that we examined.

Reliability growth models are commonly used in the Department of Defense (DoD) to plan, track, and project reliability during system acquisition and testing. We describe two commonly used classes of reliability growth models for continuous failure time data and the metrics appropriate for their use. We also present two Bayesian reliability growth models that are based on the DoD models. The Bayesian models are easily interpretable in a statistical framework, which supports estimation and uncertainty quantification. Our goal is to provide a practical understanding of the development, implementation, and use of reliability growth models across a sequence of DoD testing events. [ABSTRACT FROM AUTHOR]

In this paper, we use Markov models for studying the reliability of series systems with redundancy and repair facilities. We suppose that the units' time to failure and recovery times are exponentially distributed. We consider the cases when 1 ≤ c ≤ m and m + 1 ≤ c ≤ m + n for the system of n operating units, m unloaded redundant units and c repair facilities. Using the exponential distributions properties, we obtain stationary reliability indices of the series systems: steady-state probabilities, a stationary availability coefficient, mean time to failure, mean time between failures and mean downtime. [ABSTRACT FROM AUTHOR]

The article considers a problem to estimate a confidence interval of the main reliability indices such as availability rate, mean time between failures, and operative availability (in the stationary state) for the model of the system with duplication and independent recovery of elements.Presents a solution of the problem for a situation that often arises in practice, when there are unknown exact values of the reliability parameters of the elements, and only test data of the system or its individual parts (elements, subsystems) for reliability are known. It should be noted that the problems of the confidence estimate of reliability indices of the complex systems based on the testing results of their individual elements are fairly common function in engineering practice when designing and running the various engineering systems. The available papers consider this problem, mainly, for non-recovery systems.Describes a solution of this problem for the important particular case when the system elements are duplicated by the reserved elements, and the elements that have failed in the course of system operation are recovered (regardless of the state of other elements).An approximate solution of this problem is obtained for the case of high reliability or "fast recovery" of elements on the assumption that the average recovery time of elements is small as compared to the average time between failures.

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to analyze the reliability of the high reliability electronic and electromechanical products, a modified unilateral confidence limit analysis method under the condition of zero-failure data is proposed, which introduces the failure information into the confidence limit analysis. The failure rale of the product is estimated by Bayes parameter, and the data of the product with dilferent truncation times are obtained. The time coefficients are equivalently replaced according to the proposed method, and then the reliability of product and the mean time between failures can be known. Based on the zero-failure data of a robot stepping motor. the calculated results of the proposed method is compared with those of the other unilateral confidence limit methods. The results show that, under the confidence level of 0. 99, the mean time between failures of the product is 37 080 h, which is less than the calculated result of other confidence limit methods. The reliability of the product is estimated to be 0.973 by using the proposed new method when the mission time reaches 1 000 h. The example analysis proves the effectiveness of the new method in the re-liability evaluation of zero-failure data products, and can prevent the product relia-hilily and the mean time between failures estimation from occurrence. [ABSTRACT FROM AUTHOR]

Abstract: The Sequential Probability Ratio Test (SPRT) is widely used in the field of reliability and quality control. This paper is a continuation and a significant extension of the authors' earlier paper; it is dedicated to various risk ratios (α/β) and will lead to the increased use of the Sequential Probability Ratio Test for practical and research needs. The sample number (SN) until the test stops is a random value, and its distribution tails can be extremely long relative to the average SN (ASN). This is not suitable for practical use; therefore, truncation is required, usually by a pair of lines whose intersection, denoted as the Truncation Apex (TA), determines the maximum SN (maxSN). The optimality of the test is determined by the minimality of the SN (by means of maxSN and ASN) for a given Operating Characteristic. Presented are formulas and an algorithm for the TA and other parameters of the optimal test stopping boundaries for various α/β. This methodology also shortens the test planning process. Displacement of the TA from the optimal location results in a significant increase in ASN. The study was implemented in the Israeli standard SI‐61123. Revision of IEC 61123 and IEC 61124 (for exponential distributed data), by this study, has been accepted to the work plan of TC‐56 of IEC. The proposed methodology can be the basis for the improvement of additional standards, for example, in ISO 8422:2006. [ABSTRACT FROM AUTHOR]