Failure Times Of Components Research Articles

Unique exploration method of electronic component failure time and yield strengths of circular tubes under complete flexible model.

The aim of this study is to develop new exponential weighted moving average control charts based on a flexible model. These control charts created through least square and weighted least square estimators of the shape parameter of the new Kumaraswamy Pareto distribution. Exponential weighted moving average control charts based on least square and weighted least square estimators are compared for checking the performance of control charts. The results were not only explored through numerical values but also explored through half a dozen plots. The numerical results and plots exposed that the exponential weighted moving average control chart based on weighted least square estimator has better performance than the other proposed chart. Some key findings are discussed which are obtained from the comparative analysis of EWMA control charts. The simulation study of proposed charts is also reported in detail. The two data sets further demonstrate the effectiveness of the proposed charts. The reported results, for real data sets, are not only displayed in normal plots but also displayed in three-dimension plots. We recommend that the proposed method can be adapted for different types of distributions, and also suggest some future research directions. The concluding remarks are reported at the end of this manuscript.

Application of reinforcement learning to deduce nuclear power plant severe accident scenario

Severe accident scenarios for nuclear power plants are determined through probabilistic safety analysis (PSA). In this process, it is possible to identify the failure sequence of specific components, but assessing the impact of component failure time on the severity of accident remains a challenge. In this study, a novel approach is presented that utilizes machine learning methodologies such as reinforcement learning (RL) to complement traditional PSA. The proposed process was validated by comparing whether the most severe accident scenarios obtained through critical accident simulations can be reproduced by a RL, since this is a novel use of machine learning techniques. The comparison shows feasibility of exploring critical accident scenarios using RL. To implement the reinforcement learning methodology based on the existing system code, supervised learning model that can predict the remaining time of reactor vessel failure was implemented in this study. Based on this prediction model and data from existing catastrophic accident simulation, RL was implemented. The results obtained from RL were subsequently validated with the results of severe accident code simulation. In summary, new methodology for applying machine learning techniques to the nuclear accident analysis process was presented, and the feasibility and potential of the proposed methodology were discussed.

Reliability Assessment of Power Plant Operations through Boolean Function Expansion and Mean Time to Failure Analysis

This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system i. This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system is to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed models to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed model.

Some modified estimators for efficient estimation of power function model

AbstractThe Power Function distribution is one of the most suitable distributions for failure times modeling, especially of electric components and devices. The article proposes some new modified estimators for parameter estimation of the Power Function distribution. The proposed modified estimators are based on the mean and geometric mean of the distribution. The performance of these estimators is compared with existing traditional and modified estimators by means of Monte Carlo simulation and two real‐life examples. Total mean square error, total relative deviation, root mean square error and mean absolute error are used as performance criteria. The performance of the estimators is also compared on two real‐life data sets repressing the device failures times. From both, Monte Carlo simulation and real‐life applications, the results indicated that the proposed modified estimators perform better than the competing estimators and hence their use is recommended for parameter estimation of the Power Function probability model. The findings will help to get more precise parameter estimates for the distribution which is very much applied in different fields particularly in modelling failure times of electronic devices and components.

System reliability optimization with two-sided power distributed component failure times

Many systems connected in series are subject to failure earlier in their design life. The failure rate of such systems has been described by a two-sided power distribution, which enables modeling the entirety of the bathtub curve failure modes with a single distribution. We propose a time-dependent reliability optimization model for improving a system’s reliability through the optimal allocation of redundant components whose failure distribution follows a two-sided power distribution. To solve the multi-criteria optimization model, we propose an algorithm that (i) identifies the scale tolerance at which a selected component will have a higher mean residual life, (ii) identifies the level of redundancy for enhancing system reliability, and (iii) identifies other optimal system characteristics, including minimized cost and minimized volume.

Likelihood inference for geometric lifetimes of components of [formula omitted]-out-of-[formula omitted] systems

This paper is concerned with maximum likelihood inference based on failure times of components of a k-out-of-n system registered up to and including the moment of the breakdown of the whole system. We assume that the component lifetimes are independent and have the same geometric distribution with an unknown parameter θ. We first find the maximum likelihood estimator (MLE) of θ. Then, restricting the attention to series systems, we derive some exact and asymptotic properties of the MLE of interest. Next, we generalize our results to the situation when we observe N independently working k-out-of-n systems rather than one such system. In particular, we describe the asymptotic behavior of the MLE of θ as N grows to infinity. Finally, we illustrate our theoretical results with some examples, tables and figures.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.

Confidence Intervals for Common Coefficient of Variation of Several Birnbaum–Saunders Distributions

The Birnbaum–Saunders (BS) distribution, also known as the fatigue life distribution, is right-skewed and used to model the failure times of industrial components. It has received much attention due to its attractive properties and its relationship to the normal distribution (which is symmetric). Furthermore, the coefficient of variation (CV) is commonly used to analyze variation within a dataset. In some situations, the independent samples are collected from different instruments or laboratories. Consequently, it is of importance to make inference for the common CV. To this end, confidence intervals based on the generalized confidence interval (GCI), method of variance estimates recovery (MOVER), large-sample (LS), Bayesian credible interval (BayCrI), and highest posterior density interval (HPDI) methods are proposed herein to estimate the common CV of several BS distributions. Their performances in terms of their coverage probabilities and average lengths were investigated by using Monte Carlo simulation. The simulation results indicate that the HPDI-based confidence interval outperformed the others in all of the investigated scenarios. Finally, the efficacies of the proposed confidence intervals are illustrated by applying them to real datasets of PM10 (particulate matter ≤ 10 μm) concentrations from three pollution monitoring stations in Chiang Mai, Thailand.

A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty

While offshore wind energy is showing enormous potential, effective approaches to enhance its economics are being sought at the same time. The design of maintenance strategy is a type of strategic decision-making for offshore wind farms, aiming to improve energy production and reduce maintenance expenses. As a complicated and challenging task, the maintenance decision-making is confronted with various types of uncertainty in the model. The presence of uncertainty affects the estimation of maintenance performance, and renders the determined maintenance decisions sub-optimal or even inappropriate. In this paper, the authors propose an integrated decision-making framework incorporating i) a maintenance model which is applied to estimate maintenance performance, including maintenance costs and production losses, ii) a probabilistic uncertainty modelling approach which is used to characterize different types of uncertainty and a Monte Carlo method is adopted to generate stochastic scenarios, and iii) a multi-objective optimization method used to find the optimal decisions in the presence of conflict between multiple objectives. The uncertainties considered in the model include the stochastic attributes of time to failure, deviation between real and predicted failure times of components, and uncertain maintenance consequences. The proposed framework was applied in a generic 150MW-offshore wind farm located in the North sea. Results demonstrate that the deterministic scenario underestimates the maintenance costs and production losses, leading to the consequence that the developed maintenance strategy becomes unsatisfactory. A new series of solutions including priority solutions and trade-offs is provided for decision-makers to satisfy different goals while involving uncertainty. In addition, the influence of different uncertainties on the maintenance performance is quantified to assess the significance. The proposed optimization framework constitutes a useful decision-making tool to instruct the long-term maintenance strategy for offshore wind farms in a practical environment involving a high degree of uncertainty.

ЧАСТОТНИЙ АНАЛІЗ СИГНАЛІВ ПРИ ДІАГНОСТУВАННІ ЦИФРОВИХ ТЕЗ ЕНЕРГОДИНАМІЧНИМ МЕТОДОМ

The article analyzes the type of energy-dynamic pulses in the space-time and frequency-spectral domains to improve the diagnostic process of digital standard replacement elementswith large integrated circuits and microprocessor-based large integrated circuits in their design.Creation of new and improvement of existing systems used to determine the technical condition of digital devices in complex conditions requires the development of new methods of diagnosis. This is due to the high complexity of radio-electronic equipment and the shortage of decision time when carrying out technical state control.In the scientific and technical literature on the development and operation of radio electronic equipment, the main attention is paid to the study of the possibility of minimizing the average recovery time and cost of means of diagnostics of the radio electronic equipment object.The main factor holding back the development of modern technical diagnosis systems is the lack of a methodological apparatus capable of significantly more effective use of developed diagnostic models to identify faulty logical elements. Application of the proposed scientific approaches to the processing of diagnostic information will allow: to determine in advance the elements whose characteristics are approaching critical; to determine the technical condition of radio electronic equipment in real time; to monitor the serviceability of elements of radio electronic equipment.It will also allow for more accurate processing of diagnostic parameter values, forecasting failure times of radio electronic components with a given reliability.This, in its turn, will lead to decrease of detection time of sudden failures of radio-electronic facilities and considerably reduce time spent on restoration of digital standard replacement elements of radio-electronic equipment, requirements to which are constantly growing in conditions of repulsion of the russian aggression by the Armed Forces of Ukraine.

Residual lifetime prediction for sequential k-out-of-n systems with dependent component lifetimes

This paper deals with residual lifetimes of sequential k-out-of-n systems when component lifetimes are dependent. Given the first j component failure times, an unbiased predictor for the (j + i)-th component failure time is derived. Maximum likelihood prediction of future component failure times is also studied. Estimation of the nuisance parameters is investigated under the conditional proportional hazard rates model with the Exponential baseline distribution and the Gumbel-Hougaard copula. Illustrative examples are also given.

Availability and reliability analysis of a retrial system with warm standbys and second optional repair service

This article deals with availability and reliability characteristics for a repairable fault-tolerant system with warm standbys and second optional repair service. The failure times of primary components and warm standby ones are assumed to be exponentially distributed, and the failed components can be repaired by a repairman. For each failed component, the repairman provides two phases of repair service. All the failed components need the first essential repair, whereas only part of them demand the second optional repair. By using Markov theory and solving Matrix equation satisfied by the steady-state probabilities of the system, we present the steady-state availability of the system. By using Laplace transform method, we obtain the reliability function and the mean time to first failure of the system. Numerical examples are given to make research of the effect of the system parameters on the reliability function, steady-state availability and MTTF.

Modified moment estimators based on non-conventional measures for the power function distribution

Power function is amougst the most suitable probability models for survival or failure times analysis, particularly of electronic components and product reliability. The article proposes some new modified moment estimators for parameter estimation of the power function distribution. The proposed estimators are based on some non-conventional descriptive measures like harmonic mean, quartile deviation, Shannon entropy and Gini index. The performance of the proposed estimators is compared with the traditional moment and existing modified moment estimators. Performance is assessed through the Monte Carlo simulation and three real-life data sets representing failure and survival times of components and infected animals, respectively. Some common accuracy measures are used as performance indicators. From both, Monte Carlo simulation and all real-life applications, the results show better performance of proposed modified moment estimators based on the Gini index and harmonic mean. Hence, the use of these modified estimators is recommended for parameter estimation of the power function distribution.

A Markovian Control Approach to Maintenance Optimization of a System with Stochastic Dependence and Hidden Failures

Given partial information, this paper considers both disruption and maintenance scheduling problem for a parallel-series system with failure interactions and hidden failures. By projection on an observed history, the filtering treatment contributes to detecting an unobservable disruption time at which one of subsystems experiences failure. The model is developed by setting it in a Markovian control framework with partial information pattern in which a control process as a repair level impacts on the system availability via maintaining both the inspection and the disruption intensity at a desirable level. Since each repair and maintenance action incurs cost, the problem is to determine an optimal control process that balances the amount of maintenance and maintenance costs. The optimal control process emerges as the solution of deterministic Hamilton Jacobi equations, and a recursive scheme is used to solve them. We illustrate the procedure for the case when the failure time of components is defined as the first passage time of a Wiener process. The proposed model is illustrated through numerical examples.

On a New Modification of the Weibull Model with Classical and Bayesian Analysis

Modelling data in applied areas particularly in reliability engineering is a prominent research topic. Statistical models play a vital role in modelling reliability data and are useful for further decision-making policies. In this paper, we study a new class of distributions with one additional shape parameter, called a new generalized exponential-X family. Some of its properties are taken into account. The maximum likelihood approach is adopted to obtain the estimates of the model parameters. For assessing the performance of these estimators, a comprehensive Monte Carlo simulation study is carried out. The usefulness of the proposed family is demonstrated by means of a real-life application representing the failure times of electronic components. The fitted results show that the new generalized exponential-X family provides a close fit to data. Finally, considering the failure times data, the Bayesian analysis and performance of Gibbs sampling are discussed. The diagnostics measures such as the Raftery–Lewis, Geweke, and Gelman–Rubin are applied to check the convergence of the algorithm.

Efficient reliability analysis of dynamic k‐out‐of‐n phase‐AND mission systems

AbstractMotivated by real‐world applications of satellites and wireless sensor networks, this paper models and evaluates a dynamic k‐out‐of‐n phase‐AND mission system (k/n‐PAMS). The mission task conducted by a k/n‐PAMS involves multiple consecutive phases; the mission is successful as long as the task is successful in any of the phases. Due to factors, such as scheduled maintenance, location changes in task execution during different phases, and resource sharing with other tasks, the total number of available components n for the considered mission task and the required number of working components k may change from phase to phase. In addition, due to varying load and working environments, component failure time distributions are also phase dependent. This paper proposes an analytical modeling approach based on multivalued decision diagrams (MDDs) for assessing reliability of the considered k/n‐PAMS. The approach encompasses a new and fast MDD model generation algorithm that considers behaviors of all the mission phases simultaneously based on node labeling. As demonstrated through empirical studies on k/n‐PAMSs with different sizes (different numbers of phases and different numbers of system components), the proposed algorithm is more efficient than the traditional phase‐by‐phase model generation method.

Reliability Analysis of Nose Wheel Assy ATR 72-500

Aircraft reliability is one aspect that needs to be considered in aircraft operation. The reliability program has been implemented in aircraft maintenance policie s , especially for aircraft with high utilization. The reliability program could measure the level of reliability of a component. The nose wheel assy on the ATR 72 -500 aircraft is one of the components that has repeated defects, so that a reliability analysis could be performed. The analysis is carried out with a W eibull distribution with data of failure time . From data processing, it was obtained the value of Beta (β) = 3.810138517 which means that the failure characteristic that occurs is early wear out. In addition, the reliability value of R (t) was also obtained for every failure time. The value and graphic shows that the trend is decrease with failure time of component . From the calculation with the Weibull [M1] distribution , it also showed the Mean Time to Failure (MTBF) reaches 454.0138741 hours .

Conditions on marginals and copula of component lifetimes for signature representation of system lifetime

The signature of a system is a probability vector that depends only on the system structure. Under the classic IID (independent and identically distributed) assumption on the component lifetimes, the system lifetime distribution is the convex combination of consecutive component failure times, and the signature coordinates constitute the mixture coefficients. In this case the signature representations are very useful in determining the system lifetime distributions and for stochastic comparisons of them. This first representation was obtained in 1985 by Samaniego. Then it was extended to the more general case of exchangeable component lifetimes. In 2011 Marichal, Mathonet and Waldhauser presented necessary and sufficient conditions assuring the Samaniego representation. There were expressed in terms of distributional properties of families of auxiliary indicator random vectors parametrized by positive numbers. In the paper we obtain other necessary and sufficient conditions represented in terms of the marginal distributions of component lifetimes and the dependence copula of them. Moreover, we study symmetry conditions for the equality of structural and probabilistic signatures.

Reliability model of the RTC System with trackside equipment equipped with a Peltier cell

The article presents a mathematical model assessing the reliability of rail traffic control systems with a mounted Peltier cell. The semiconductor element will be used for operating electronic components that are in the trackside equipment. In the prepared model, Markow processes were used to describe the states occurring between individual components of the RTC System. Simulation tests were mainly based on determining the average time of component failure. The presented results were carried out for 1,000, 2,000 and 3,000 rail vehicle crossings over the railway tracks equipped with RTC Systems. At the very end of the article the conclusions resulting from computer simulations were presented.

Comparative Analysis of the Reliability of Instrumentation and Control Systems Using the Signature Theory

At the present, in the comparative analysis of design decisions from the point of view of system architecture, a qualitative and quantitative analysis of the indicators for each solution is performed followed by a comparison of the results. The classical method of calculating reliability is very laborious due to the need to take into account the properties of all elements of the system. One of the tools to quantify the reliability of systems consisting of components with random failure rates that are independent and equally distributed is signature theory. The signature describes the structure of the system and can be combined with ordinal statistics of the component failure time to form conclusions about the reliability of the system and compare several systems according to this criterion. The article provides a comparative analysis of the safety of instrumentation and control system using the classical method of calculating reliability indicators as well as using signature theory. As an example, the article considers a protection system that includes the initiating and executive parts. The system architecture is presented in several simplified versions. The dependences of the probabilities of failures of the “failure on demand” and “spurious actuations” type for each of the architecture implementation variants are obtained. It is concluded that, with the exponential distribution of MTBF, there is a complete correlation of the results of circuit comparison performed using both the classical method of calculating reliability (based on the failure rates of components) and using signature theory. Moreover, the use of signature theory greatly simplifies comparing the reliability of systems and networks consisting of components of the same type.