Reliability modeling for competing failure processes considering degradation rate variation under cumulative shock

Most systems experience both random shocks (hard failure) and performance degradation (soft failure) during service span, and the dependence of the two competing failure processes has become a key issue. In this study, a novel dependent competing failure processes (DCFPs) model with a varying degradation rate is proposed. The comprehensive impact of random shocks, especially the effect of cumulative shock, is reasonably considered. Specifically, a shock will cause an abrupt degradation damage, and when the cumulative shock reaches a predefined threshold, the degradation rate will change. An analytical reliability solution is derived under the concept of first hitting time (FHT). Besides, a one-step maximum likelihood estimation method is established by constructing a comprehensive likelihood function. Finally, the reasonability of the closed form reliability solution and the feasibility and effectiveness of the proposed DCFPs modeling methodology are demonstrated by a comparative simulation study.     

基于GaAs激光器性能退化的可靠性度量方法

针对传统可靠性度量方法对高可靠性及长寿命的激光产品进行可靠性度量存在因模型假设不准确而出现可靠性度量错误风险的问题,基于性能退化轨迹提出了利用非参数局部线性回归估计对实际的退化模型进行直接估计的方法.该方法在确定实际模型后,利用失效阈值外推获得伪失效寿命时间,进而采用完全寿命时间数据进行可靠性度量.最后通过对GaAs激光器的退化数据进行可靠性验证分析,结果表明此方法提高了可靠性的预测精度,拟合程度高,稳健性好.采用非参数局部线性回归估计方法得到的结果合理、准确.     

Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation

This paper presents an extension of reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation. The probability that a product fails on a specific mode is derived. Using this probability, the dominant failure mode on the product can be predicted. A practical example is presented to analyze an electronic device with two kinds of major failure modes–solder/Cu pad interface fracture (a catastrophic failure) and light intensity degradation (a degradation failure). Reliability modeling of an individual failure mode and device reliability analysis is presented and results are discussed. Copyright © 2003 John Wiley & Sons, Ltd.     

Testing and analysing capability performance for products with multiple characteristics

Process capability analysis is a vital part of an overall quality improvement programme. Numerous techniques and tools have been proposed for process capability analysis. Among these, indices and charts of process capability are simple and effective tools and widely used in the manufacturing industry. Many scholars have revealed numerous valuable aspects of previously developed tools and methods. Due to the rising demands of product quality, the current tools and methods are insufficient for enabling managers to make informed decisions. To address this gap, this study proposes a hypothesis testing procedure which determines whether the process capabilities satisfy the target level. Furthermore, this study proposes an integrated quality test chart (IQTC), which can display the process potential and performance for an entire product with smaller-the-better, larger-the-better and nominal-the-best specifications. The proposed procedure and IQTC incorporate the quality-level concept of the Six Sigma model and can be used to quantitate the relationships among the quality level, capability indices and process yield. They can be applied to assist managers in measuring, monitoring, analysing and improving process performance in a timely manner which will help ensure that the quality levels of their products meet customer demands. Finally, an example is provided to illustrate how to use the proposed procedure and IQTC.     

Real‐time Reliability Evaluation with a General Wiener Process‐based Degradation Model

The aim of this paper is to investigate the issue of real‐time reliability evaluation based on a general Wiener process‐based degradation model. With its mathematical tractability, the Wiener process with a linear drift has been widely used in the literature, to characterize the dynamics of the degradation process or its transformation. However, the nonlinear degradation process, which can't be properly linearized, exists in practice. The dynamics of such a degradation process can't be accurately captured by linear models. Here, a general Wiener process‐based degradation model is proposed, which covers a variety of Wiener process‐based models as its limiting cases. A two‐stage method is presented to estimate the unknown parameters. Two real‐time reliability evaluation procedures are presented for different conditions: one is the analytical evaluation procedure, and the other is the simulated evaluation procedure. It is shown that when new degradation information is available, the evaluation results can be adaptively updated. Moreover, to check out the proposed degradation model, a graphical method is provided. Finally, the validity of the proposed evaluation method is illustrated by a numerical example and two real‐world examples. Copyright © 2013 John Wiley & Sons, Ltd.     

基于Gamma退化过程的直升机主减速器行星架剩余寿命预测模型

鉴于Gamma过程具有平稳、独立增量等退化建模所需的属性,将其用于描述设备退化过程,并针对缺乏故障数据时难以进行剩余寿命预测的问题,利用设备运行中采集的表征其退化状态的大量间接状态参数和少量直接状态参数,建立了基于Gamma退化过程的剩余寿命预测模型;针对经验最大化算法中似然函数难以解析求解的问题,引入粒子滤波算法实现了模型参数估计;最后将模型应用于直升机主减速器行星架的剩余寿命预测,得到了不同时刻的预测结果及95%置信区间,验证了预测模型的有效性和准确性。     

Reliability modeling for consecutive k-out-of-n: F systems with local load-sharing components subject to dependent degradation and shock processes

Traditional k-out-of-n models assume that the components are independent, while recent research studies assume that the components are dependent caused by global load-sharing characteristic. In this paper, we investigate the consecutive k-out-of-n systems with dependent components by local load-sharing characteristic. The work load and shock load on failed components will be equally shared by adjacent components, so the components tend to fail consecutively. Consequently, the components degradation processes may be diverse, since their degradation rate (dependent on work load) and abrupt degradation (dependent on shock load) become unequal because of local load-sharing effect. Furthermore, the system failure will be path-dependent on the failure sequences of components, which results in that the same system states may have different system failure probabilities. This new dependence makes the system reliability model more complex. In this work, an analytical model that can be solved numerically is derived to compute the reliability with this complex dependence. The developed model is demonstrated by a cable-strut system in the suspension bridge. The results show that the reliability decreases significantly when the new dependence is considered.     

Reliability analysis for accelerated degradation data based on the Wiener process with random effects

On the basis of the principle of degradation mechanism invariance, a Wiener degradation process with random drift parameter is used to model the data collected from the constant stress accelerated degradation test. Small-sample statistical inference method for this model is proposed. On the basis of Fisher's method, a test statistic is proposed to test if there is unit-to-unit variability in the population. For reliability inference, the quantities of interest are the quantile function, the reliability function, and the mean time to failure at the designed stress level. Because it is challenging to obtain exact confidence intervals (CIs) for these quantities, a regression type of model is used to construct pivotal quantities, and we develop generalized confidence intervals (GCIs) procedure for those quantities of interest. Generalized prediction interval for future degradation value at designed stress level is also discussed. A Monte Carlo simulation study is used to demonstrate the benefits of our procedures. Through simulation comparison, it is found that the coverage proportions of the proposed GCIs are better than that of the Wald CIs and GCIs have good properties even when there are only a small number of test samples available. Finally, a real example is used to illustrate the developed procedures.     

Reliability and performance analysis for fault-tolerant programs consisting of versions with different characteristics

This paper presents a simple straightforward algorithm for evaluating reliability and expected execution time for software systems consisting of fault-tolerant components. The components are built from functionally equivalent but independently developed versions characterized by different reliability and performance. Both N-version programming (with parallel and sequential execution of the versions) and the recovery block scheme are considered within a universal model.     

Electro-mechanical modeling of Nb3Sn CICC performance degradation due to strand bending and inter-filament current transfer

Performance degradation of Nb₃Sn cable-in-conduit conductors (CICCs) is a critical issue in large-scale magnet design such as the International Thermonuclear Experimental Reactor (ITER) and the series-connected hybrid (SCH) magnets currently under development at the National High Magnetic Field Laboratory (NHMFL). Not only the critical current is significantly lower than expectations but also the voltage-current characteristic is observed to have a much broader transition from a single strand to a CICC cable. The variation of conductor voltage-current characteristic as a result of cable electromagnetic, mechanical and thermal interactions is challenging to model. In this paper, we use a new numerical model, called the Florida electro-mechanical cable model (FEMCAM) benchmarked against 40 different conductor tests, to study the influence of bending strain and current non-uniformity on the critical current and n-value of Nb₃Sn strands and CICC cables. The new model combines thermal bending effects during cool-down, electromagnetic bending effects during magnet operation and current transfer in strands with filament fracture. The n-value of a strand under bending is derived from integration of filament critical current over strand cross-section for full and no current transfer. The cable n-value is obtained from the power law relation of cable electric field and critical current curve. By comparing numerical results with measurements of advanced Nb₃Sn strands and various CICC cables, we demonstrate that FEMCAM is self-consistent in modeling inter-filament current transfer. The new model predicts that I_c degradation of bent strands initially follows closely full current transfer but starts deviating and falls between full and no current transfer with an increasing bending strain. The results agree with recent TARSIS measurements for less than 1% bending strain mostly interested in practice. The strand n-value degradation from FEMCAM with no filament current transfer agrees better with measurements than that from full current transfer. Finally, FEMCAM simulated cable n-values are compared with various CICC measurements. The results imply that FEMCAM is a useful tool for the design of Nb₃Sn-based CICCs and both thermal bending and electromagnetic bending play important roles in CICC performance.     

Engineering‐driven performance degradation analysis of hydraulic piston pump based on the inverse Gaussian process

As a key aircraft component, hydraulic piston pumps must be developed with high reliability. However, collecting failure time data of such pumps for reliability analysis is a big challenge. To save testing time, performance degradation data obtained from degradation tests can be used for quick reliability estimation of hydraulic piston pumps. This paper proposes an engineering‐driven performance degradation analysis method considering the nature of mechanical wear of hydraulic piston pumps. First, the failure mechanism of a type of hydraulic piston pump is investigated. By taking into account the close relationship between the degradation rate and the failure mechanism, an inverse Gaussian (IG) process model with a variable rate is developed to describe the degradation behavior of the pump. Under this model, a Bayesian statistical method is developed for degradation data analysis. The corresponding procedure for model parameter estimation and reliability evaluation is also presented. The proposed degradation analysis method is illustrated using a real experimental data. The results show that the engineering‐driven approach is quite effective in evaluating the lifetime of the hydraulic piston pump and will improve the overall reliability of aircraft operation in the field.     

行波热声发动机的多负载声功输出特性

为进一步提高热声发动机的声功输出能力并为驱动多负载做准备,提出了热声发动机声功的多路引出方案。实验中以氮气为工质,工作压力为2．4MPa,在热声发动机环路压比稳定在1．20的情况下,从环路声容和谐振管处同时引出声功,获得了389．95W的最大声功,11．3％的最大声功输出效率以及16．0％的最大声功输出炯效率,这比单独从环路引出的最大声功和效率分别提高了51．4％,24．6％以及19．4％。     

Quality evaluation of internal cylindrical grinding process with multiple quality characteristics for gear products

Gears are among the most crucial components in the transmission systems of machine tools. Gear manufacturing includes a number of processing procedures. The grinding process is an important procedure involving high precision and fairly small grinding surfaces. For this reason, this study aimed at developing a quality assessment model for the internal cylindrical grinding process of gears. The Six Sigma quality indices (SSQIs) were used to directly assess the quality of the internal cylindrical grinding process due to their ability to directly reflect quality level and process yield. Since the process may include nominal-the-best (NTB), larger-the-better (LTB) and smaller-the-better (STB) quality characteristics, so we used the variable transformation method to normalise the specifications of each quality characteristic for the convenient and effective management and analysis of process performance for multiple quality characteristics. We then constructed a multi-characteristic process quality analysis chart (MPQAC) to simultaneously assess the quality levels of various quality characteristics. Furthermore, the MPQAC can provide references for process improvement. This ensures the quality of internal cylindrical grinding and enhances the quality of gear and machine tool products. Finally, a real-world application and numerical experiments demonstrate the effectiveness and practical applicability of the proposed method.     

基于EMD多尺度威布尔分布与HMM的轴承性能退化评估方法

轴承作为旋转机械中的重要部件,对其性能退化状态进行准确评估是开展预测性维护的重要前提。针对现有性能退化指标在鲁棒性和敏感性上的不足,提出一种基于多尺度威布尔分布与隐马尔可夫模型(Hidden Markov model,HMM)的滚动轴承性能退化评估方法。首先,采用经验模态分解(empirical mode decomposition,EMD)对轴承振动信号进行多尺度分解,将轴承振动数据分解到不同尺度的本征模态分量(intrinsic mode function,IMF)中;然后,通过峭度指标选取故障特征信息明显的IMF分量,并对各个IMF分量进行滑动窗口威布尔分布拟合,提取多尺度威布尔形状参数作为性能退化特征;最后,将轴承正常状态下退化特征参数输入隐马尔可夫模型(Hidden Markov model,HMM)进行训练,建立性能退化评估模型,从而实现轴承性能退化评估。试验结果表明,该评估方法可以有效反映轴承的性能退化趋势,与其他相关方法相比,该方法能够及时识别到轴承早期故障,并且具有较强的稳定性。     

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.     

Reliability assessment of high‐quality and long‐life products based on zero‐failure data

For a period of mission time, only zero‐failure data can be obtained for high‐quality long‐life products. In the case of zero‐failure data reliability assessment, the point estimates and confidence interval estimates of distribution parameters cannot be obtained simultaneously by the current reliability assessment models, and the credibility of the assessment results may be reduced if they are obtained at the same time. A new model is proposed for consistency problem in this paper. In the proposed model, the point estimates of reliability can be obtained by the lifetime probability distribution derived from matching distribution curve method, while the confidence interval estimates of reliability can be obtained by using new samples generated from the lifetime probability distribution according to parameter bootstrap method. By analyzing the zero‐failure data of the torque motors after real operation, the results show that the new model not only meets the requirements of reliability assessment but also improves the accuracy of reliability interval estimation.     

The development of a target‐focused process capability index with multiple characteristics

Within an industrial manufacturing environment, Process Capability Indices (PCIs) enable engineers to assess the process performance and ultimately improve the product quality. Despite the fact that most industrial products manufactured today possess multiple quality characteristics, the vast majority of the literature within this area primarily focuses on univariate measures to assess process capability. One particular univariate index, C_pm, is widely used to account for deviations between the location of the process mean and the target value of a process. While some researchers have sought to develop multivariate analogues of C_pm, modeling the loss in quality associated with multiple quality characteristics continues to remain a challenge. This paper proposes a multivariate PCI that more appropriately estimates quality loss, while offering greater flexibility in conforming to various industrial applications, and maintaining a more realistic approach to assessing process capability. Copyright © 2010 John Wiley & Sons, Ltd.     

滚动轴承的振动性能退化过程与保持可靠性研究

为了分析滚动轴承的振动性能退化过程与保持可靠性之间的关系,提出一种基于最大熵法和相似度法的滚动轴承振动性能退化模型,并对滚动轴承的退化过程进行评估;然后基于最大熵法和泊松过程建立滚动轴承振动性能保持可靠性模型;最后基于灰关系理论对滚动轴承的性能退化序列和保持可靠性序列进行分析。实验结果表明,所提出的滚动轴承振动性能退化模型能够有效地识别滚动轴承的退化状态,并且滚动轴承振动性能保持可靠性的演变历程与振动性能退化过程之间有明显的灰关系,可信水平均达到80%以上。     

基于小动物荧光成像研究生物材料体内降解的实验方法

可降解材料作为生物材料的重要组成部分,其体内降解性能的好坏往往决定着材料植入后的成败.因此,对材料体内降解的评价显得尤为重要.传统的生物材料体内降解评价方法需要在各取样点取出不同批次的降解样品,阻止了对同一个实验样品降解过程的连续测量,并且存在样品需求量大的问题.小动物活体成像系统(in vivo imaging sy...     

A Bayesian framework for uncertainty quantification of perturbed gamma process based on simulated likelihood

The perturbed gamma process (PGP) has recently been widely used in modeling the noisy degradation data collected from engineering structures and components since it can simultaneously consider the temporal variability of degradation and measurement uncertainty. As a result of the sampling and inspection uncertainty in engineering practice, it is necessary to account for the resulting parameter uncertainty. Meanwhile, the flexibility of the form of measurement error motivates a potential demand for quantifying the model uncertainty and selecting the most fitting error model for the given inspection data. The Bayesian approach is well-suited to quantity the parameter uncertainty induced by imperfect inspection and limited inspection data, but its practical implementation is extremely challenging due to the intractable likelihood function of PGP. In the paper, a novel Bayesian framework for quantifying parameter and model uncertainty of PGP is presented, where the simulated likelihood that is an unbiased estimator generated by Sequential Monte Carlo (SMC) is introduced to overcome the intractable likelihood of PGP. More specifically, an Adaptive Particle Markov chain Monte Carlo (APMCMC) is proposed to perform the Bayesian sampling from the posterior distributions of parameters, achieving the requirement for the quantification of parameter uncertainty. By utilizing the posterior samples from APMCMC, a model selection method based on the Bayes factor is employed to determine the most fitting one from some alternative error models. Finally, two simulation examples are presented to illustrate the efficiency and accuracy of the proposed framework and its applicability is confirmed by a practical case involving the corrosion modeling of a group of pipelines.