基于故障数据的设备运行可靠性分析与评估

基于故障数据,对设备运行可靠性进行了分析与评估。对某汽车制造企业的一台卧式加工中心的故障数据进行了统计与分析,形成观测样本,并拟合出了设备故障间隔时间的概率密度分布函数和累计分布函数曲线,从而推断得出其分布规律可能服从威布尔分布。然后通过对威布尔分布函数相关性进行检验,验证了该设备的故障间隔时间分布服从威布尔分布。最后根据统计结果计算得出了该设备的各项可靠性评估指标。     

A Bayesian zero‐failure reliability demonstration test of high quality electro‐explosive devices

Usually, for high reliability products the production cost is high and the lifetime is much longer, which may not be observable within a limited time. In this paper, an accelerated experiment is employed in which the lifetime follows an exponential distribution with the failure rate being related to the accelerated factor exponentially. The underlying parameters are also assumed to have the exponential prior distributions. A Bayesian zero‐failure reliability demonstration test is conducted to design forehand the minimum sample size and testing length subject to a certain specified reliability criterion. Probability of passing the test design as well as predictive probability for additional experiments is also derived. Sensitivity analysis of the design is investigated by a simulation study. Copyright © 2009 John Wiley & Sons, Ltd.     

Reliability qualification of semiconductor devices based on physics‐of‐failure and risk and opportunity assessment

Qualification frequently is a time‐critical activity at the end of a development project. As time‐to‐market is a competitive issue, the most efficient qualification efforts are of interest. A concept is outlined, which proactively integrates qualification into the development process and provides a systematic procedure as a support tool to development and gives early focus on required activities. It converts requirements for a product into measures of development and qualification in combination with a risk and opportunity assessment step and accompanies the development process as a guiding and recording tool for advanced quality planning and confirmation. The collected data enlarge the knowledge database for DFR/BIR (designing for reliability/building‐in reliability) to be used for future projects. The procedure challenges and promotes teamwork of all the disciplines involved. Based on the physics‐of‐failure concept the reliability qualification methodology is re‐arranged with regard to the relationships between design, technology, manufacturing and the different product life phases at use conditions. It makes use of the physics‐of‐failure concept by considering the potential individual failure mechanisms and relates most of the reliability aspects to the technology rather than to the individual product design. Evaluation of complex products using common reliability models and the definition of sample sizes with respect to systematic inherent product properties and fractions of defects are discussed. Copyright © 2002 John Wiley & Sons, Ltd.     

Reliability Analysis of Zero‐Failure Data with Poor Information

For costly and dangerous experiments, growing attention has been paid to the problem of the reliability analysis of zero‐failure data, with many new findings in world countries, especially in China. The existing reliability theory relies on the known lifetime distribution, such as the Weibull distribution and the gamma distribution. Thus, it is ineffective if the lifetime probability distribution is unknown. For this end, this article proposes the grey bootstrap method in the information poor theory for the reliability analysis of zero‐failure data under the condition of a known or unknown probability distribution of lifetime. The grey bootstrap method is able to generate many simulated zero‐failure data with the help of few zero‐failure data and to estimate the lifetime probability distribution by means of an empirical failure probability function defined in this article. The experimental investigation presents that the grey bootstrap method is effective in the reliability analysis only with the few zero‐failure data and without any prior information of the lifetime probability distribution. Copyright © 2011 John Wiley & Sons, Ltd.     

Stress‐Strength Reliability Analysis with Extreme Values based on q‐Exponential Distribution

When dealing with practical problems of stress–strength reliability, one can work with fatigue life data and make use of the well‐known relation between stress and cycles until failure. For some materials, this kind of data can involve extremely large values. In this context, this paper discusses the problem of estimating the reliability index R = P(Y < X) for stress–strength reliability, where stress Y and strength X are independent q‐exponential random variables. This choice is based on the q‐exponential distribution's capability to model data with extremely large values. We develop the maximum likelihood estimator for the index R and analyze its behavior by means of simulated experiments. Moreover, confidence intervals are developed based on parametric and nonparametric bootstrap. The proposed approach is applied to two case studies involving experimental data: The first one is related to the analysis of high‐cycle fatigue of ductile cast iron, whereas the second one evaluates the specimen size effects on gigacycle fatigue properties of high‐strength steel. The adequacy of the q‐exponential distribution for both case studies and the point and interval estimates based on maximum likelihood estimator of the index R are provided. A comparison between the q‐exponential and both Weibull and exponential distributions shows that the q‐exponential distribution presents better results for fitting both stress and strength experimental data as well as for the estimated R index. Copyright © 2016 John Wiley & Sons, Ltd.     

基于故障危害度的飞机电源系统可靠性评估

基于故障危害度对飞机电源系统进行可靠性评估,首先建立可靠性评估模型,该模型考虑各故障样本对飞机电源系统供电可靠性危害度影响,将出现的故障按照对电源系统安全、性能、任务及维修等指标的影响程度进行等级分类,从故障的失效机理出发建立相应的分布模型;然后采用分布计算和二次分布等算法进行系统整体可靠性指标评估。应用该模型对飞机电源系统进行了故障统计分析,计算了飞机电源系统的可靠性指标。     

Reliability estimation based on operational data of manufacturing systems

Maintenance management has a direct influence on equipment reliability and safety. However, a large portion of traditional maintenance models and reliability analysis methods usually assumes that only perfect maintenance is performed on the system and the system will restore to as good as new regardless of the kind of preventive maintenance work‐order that is performed. This is not practical in reality and may result in an inaccurate parametric estimation. The research objective of this paper is to develop a maximum likelihood estimation method to obtain more accurately estimated parameters based on the operational data of manufacturing systems, taking into consideration the difference between perfect and imperfect maintenance work‐orders. Weibull distribution is specifically studied for this purpose. A practical case study based on industrial operational data from an automotive assembly line is performed to illustrate the implementation and efficiency of the proposed reliability estimation method. Copyright © 2008 John Wiley & Sons, Ltd.     

Automotive reliability prediction based on early field failure warranty data

This paper provides a reliability prediction method to identify vehicle components that have the potential to become actionable items (such as a recall decision) based on their early field failure (4 or 5 months in service) warranty data. The vehicle customer mileage distribution from the warranty database is also discussed utilizing a mathematical model known as the lognormal distribution. The applicability of the above prediction method is demonstrated. © 1998 John Wiley & Sons, Ltd.     

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment. The common cause failure (CCF) is simultaneous failure of multiple elements in a system under a common cause, and it is a common phenomenon in engineering systems with dependent elements. Several models and methods have been proposed for modeling and assessment of complex systems with CCF. In this paper, a new reliability assessment method is proposed for the systems suffering from CCF in a dynamic environment. The CCF among components is characterized by a BN, which allows for bidirectional reasoning. A proportional hazards model is applied to capture the dynamic working environment of components and then the reliability function of the system is obtained. The proposed method is validated through an illustrative example, and some comparative studies are also presented.     

Reliability inference for VGA adapter from dual suppliers based on contaminated type‐I interval‐censored data

Type‐I interval‐censoring scheme only documents the number of failed units within two prespecified consecutive exam times at the larger time point after putting all units on test at the initial time schedule. It is challenging to use the collected information from type‐I interval‐censoring scheme to evaluate the reliability of unit when not all admitted units are operated or tested at the same initial time and a majority of units are randomly selected to replace the failed test units at unrecorded time points. Moreover, the lifetime distribution of all pooled units from dual resources usually follows a mixture distribution. To overcome these two problems, a two‐stage inference process that consists of a data‐cleaning step and a parameter estimation step via either Markov chain Monte Carlo (MCMC) algorithm or profile likelihood method is proposed based on the contaminated type‐I interval‐censored sample from a mixture distribution with unknown proportion. An extensive simulation study is conducted under the mixture smallest extreme value distributions to evaluate the performance of the proposed method for a case study. Finally, the proposed methods are applied to the mixture lifetime distribution modeling of video graphics array adapters for the support of reliability decision.     

Application of Bayesian Methods for Age‐Dependent Reliability Analysis

In this article, the authors present a general methodology for age‐dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age‐dependent change of beliefs about reliability parameters (mainly failure rate), when change of belief occurs not only because new failure data or other information becomes available with time but also because it continuously changes due to the flow of time and the evolution of beliefs. The main objective of this article is to present a clear way of how practitioners can apply Bayesian methods to deal with risk and reliability analysis considering ageing phenomena. The methodology describes step‐by‐step failure rate analysis of ageing components: from the Bayesian model building to its verification and generalization with Bayesian model averaging, which as the authors suggest in this article, could serve as an alternative for various goodness‐of‐fit assessment tools and as a universal tool to cope with various sources of uncertainty. The proposed methodology is able to deal with sparse and rare failure events, as is the case in electrical components, piping systems and various other systems with high reliability. In a case study of electrical instrumentation and control components, the proposed methodology was applied to analyse age‐dependent failure rates together with the treatment of uncertainty due to age‐dependent model selection. Copyright © 2013 John Wiley & Sons, Ltd.     

A cumulative damage model for fatigue life estimation of high‐strength steels in high‐cycle and very‐high‐cycle fatigue regimes

A cumulative fatigue damage model is presented to estimate fatigue life for high‐strength steels in high‐cycle and very‐high‐cycle fatigue regimes with fish‐eye mode failure, and a simple formula is obtained. The model takes into account the inclusion size, fine granular area (FGA) size, and tensile strength of materials. Then, the ‘equivalent crack growth rate’ of FGA is proposed. The model is used to estimate the fatigue life and equivalent crack growth rate for a bearing steel (GCr15) of present investigation and four high‐strength steels in the literature. The equivalent crack growth rate of FGA is calculated to be of the order of magnitude of 10^?14–10^?11 m/cycle. The estimated results accord well with the present experimental results and prior predictions and experimental results in the literature. Moreover, the effect of inclusion size on fatigue life is discussed. It is indicated that the inclusion size has an important influence on the fatigue life, and the effect is related to the relative size of inclusion for FGA. For the inclusion size close to the FGA size, the former has a substantial effect on the fatigue life. While for the relatively large value of FGA size to inclusion size, it has little effect on the fatigue life.     

Fault trees analysis using expert opinion based on fuzzy‐bathtub failure rates

The main objective of fault tree analysis method is to estimate the “Top Event occurrence probability”. This requires determination of failure time distribution functions also known as “Bathtub Curves” for each of the system elements/events. This paper introduces a novel method to determine the failure time distribution functions using possibility theory. For this purpose, fuzzy‐bathtub distributions using expert opinions are generated for basic events and fuzzy formulas are derived for static and dynamic gates fault tree constructions. This process completed by proposed fuzzy Monte Carlo simulation throughout the preferred operational time and uses the actual time‐to‐failure data. Accordingly, the Top Event failure curve and the reliability profile of the system are depicted based on the defuzzificated basic‐events' bathtub‐failure‐rates. The results show that the proposed method not only is feasible and powerful but can also be accurate more than the other probabilistic and possibilistic techniques because of the component failure rates follow the real failure distributions.     

A note on MTTF formulae for redundant systems with Erlangian‐distributed unit and common‐cause times to failure

This communication presents newly developed mean‐time‐to‐failure (MTTF) formulae for systems with Erlangian‐distributed unit and common‐cause times to failure. These systems are parallel, triple modular redundant (TMR), r‐out‐of‐m, bridge, parallel‐series and series‐parallel. Copyright © 2000 John Wiley & Sons, Ltd.     

Reliability study of fracture mechanics based non‐probabilistic interval analysis model

In this paper, we advanced a new interval reliability analysis model for fracture reliability analysis. Based on the non‐probabilistic stress intensity factor interference model and the ratio of the volume of the safe region to the total volume of the region associated with the variation of the standardized interval variables is suggested as the measure of structural non‐probabilistic reliability. We use this theory to calculate the reliability of structure based on fracture criterion. This model needs less uncertain information, so it has less limitation for analysing an uncertain structure or system. Examples of practical application are given to explain the simplicity and practicability of this model by comparing the interval reliability analysis model with probabilistic reliability analysis model.     

A study of time‐between‐events control chart for the monitoring of regularly maintained systems

Owing to usage, environment and aging, the condition of a system deteriorates over time. Regular maintenance is often conducted to restore its condition and to prevent failures from occurring. In this kind of a situation, the process is considered to be stable, thus statistical process control charts can be used to monitor the process. The monitoring can help in making a decision on whether further maintenance is worthwhile or whether the system has deteriorated to a state where regular maintenance is no longer effective. When modeling a deteriorating system, lifetime distributions with increasing failure rate are more appropriate. However, for a regularly maintained system, the failure time distribution can be approximated by the exponential distribution with an average failure rate that depends on the maintenance interval. In this paper, we adopt a modification for a time‐between‐events control chart, i.e. the exponential chart for monitoring the failure process of a maintained Weibull distributed system. We study the effect of changes on the scale parameter of the Weibull distribution while the shape parameter remains at the same level on the sensitivity of the exponential chart. This paper illustrates an approach of integrating maintenance decision with statistical process monitoring methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Fatigue life analysis of automotive key parts based on improved peak‐over‐threshold method

To eliminate the limitations of the peak‐over‐threshold (POT) extrapolation method, the paper proposes an improved POT extrapolation method to reconstruct the load sequence and frequency of the part. A threshold model is established based on a generalized Pareto distribution (GPD). Assuming a road surface's irregularity obeys a normal distribution, the load between the upper and lower thresholds is reconstructed to obtain more dynamic road load data. Rain‐flow counting method is used to calculate the peak‐to‐valley value and frequency of the load signal, and the characteristics of the load mean and amplitude before and after the improvement of the POT extrapolation method were discussed. The effectiveness and conservativeness of the enhanced method are verified by predicting the fatigue life of a swing support rod.     

Stress‐life prediction of 25°C polypropylene materials based on calibration of Zhurkov fatigue life model

In this study, to evaluate the chemical and mechanical properties of polypropylene (PP), activation‐energy and tensile tests were performed at room temperature (25°C) on pure PP and PP reinforced with glass fibre (GF). To improve the prediction accuracy of the fatigue life, three models based on the calibration of the Zhurkov model were proposed: a regression model, modified strain‐rate model and lethargy coefficient‐based model. Based on the experimental data analysis and statistical assessment results, we proposed a modified strain‐rate model that satisfies the dependency of the physical parameters and is congruent with the predicted fatigue life data. The experimental data and modified strain‐rate model were compared with the direct cyclic analysis results. The tendency of the frequency factor as a correction parameter in the modified strain‐rate model corresponded to the experimental activation energy and the increasing GF content.     

Plastic collapse load prediction and failure assessment diagram analysis of cracked circular hollow section T‐joint and Y‐joint

This paper concerns the validation of standard safety assessment procedure given in BS 7910 for cracked circular hollow section T‐joint and Y‐joint, using the finite element (FE) results. A robust and efficient FE mesh generator is developed to produce the 3D models of the cracked joints and to calculate the elastic J‐integral (J_e) and elastic–plastic J‐integral (J_ep) values of the crack respectively. In order to verify its accuracy and convergence, the plastic collapse loads (P_c) obtained from experimental tests and FE predictions are compared; they agree very well with each other. It is also found from experimental tests that the plastic collapse loads (P_c) predicted using the BS 7910 reduction factor (F_AR) are safe and conservative. Subsequently, the failure assessment diagrams (FADs) of five cracked T‐joints and three cracked Y‐joints are constructed using the FE results, following the J‐integral method, which is classified as Level 3C in BS 7910. Thereafter, a comparison between the constructed FAD curves and the standard Level 2A curve is carried out, and it is observed that the safety assessment results using the standard Level 2A curve might be unsafe because some parts of the constructed FAD curves fall inside of the standard one. A penalty factor of 1.15 working on both the elastic–plastic J‐integral and plastic collapse load (P_c) is proposed to move all the constructed FAD curves just outside of the standard Level 2A curve.     

Copula‐function‐based analysis model and dynamic reliability of a gear transmission system considering failure correlations

In this study, an improved prediction model is introduced to assess the residual strength of gear transmission systems. The residual strength and stress‐strength interference theories are then combined to facilitate the study of the reliability of the gears and bearings in these systems. Based on the copula function, the correlation between the gear contact and bending failure is determined. The relationship between the copula function and reliability is used to determine the dynamic reliability of the gears while considering multiple correlated failure modes. In this manner, more accurate and dynamic reliability attenuation characteristics are obtained for the gear transmission system.