δ冲击条件下相关性竞争失效过程的系统可靠性建模

研究系统受到δ冲击时,考虑系统自然退化和冲击两个竞争性失效过程间具有相关性时,系统可靠性的建模问题。相关性一方面表现为冲击造成系统退化量的增加,另一方面表现为系统的自然退化程度对冲击结果的影响。假设系统因冲击而失效的过程是δ冲击过程,通过系统自然退化过程和冲击过程的分布函数,导出了系统的可靠度函数,建立了系统可靠度模型的一般形式,并给出一种特例的具体形式,最后利用文献中的具体参数进行仿真,以验证模型的正确性和有效性。     

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.     

Reliability analysis for multi-state systems subject to distinct random shocks

This paper analyzes the competing and dependent failure processes for multi-state systems suffering from four typical random shocks. Reliability analysis for discrete degradation is conducted by explicitly modeling the state transition characteristics. Semi-Markov model is employed to explore how the system vulnerability and potential transition gap affect the state residence time. The failure dependence is specified as that random shocks can not only lead to different abrupt failures but also cause sudden changes on the state transition probabilities, making it easier for the system to stay at the degraded states. Reliability functions for all the exposed failure processes are presented based on the corresponding mechanisms. Interactions between different failure processes are also taken into account to evaluate the actual reliability levels in the context of degradation and distinct random shocks. An illustrative example of a multi-state air conditioning system is studied to demonstrate how the proposed method can be applied to the engineering practice.     

基于比例危险模型的产品竞争失效分析

突发型失效与退化型失效共存的竞争失效问题在实践中大量存在，一般情况下突发失效是受退化量大小影响的。文中利用比例危险模型分析了突发失效与退化量的关系，给出了竞争失效的一般模型及模型的参数估计方法，最后利用所给模型对强激光装置所用的金属化膜脉冲电容器进行了可靠性分析。     

Reliability assessment models for dependent competing failure processes considering correlations between random shocks and degradations

This article develops reliability models for systems subject to two dependent competing failure processes, considering the correlation between additional damage size on degradation in soft failure process and stress magnitude of shock load in hard failure process, both of which are caused by the same kth random shock. The generalized correlative reliability assessment model based on copulas is proposed, which is then extended to three different shock patterns: (1) δ‐shock, (2) m‐shock, and (3) m‐run shocks. There are some statistical works to be introduced in reliability modeling, including data separation of total degradation amount, inferring the distribution of amount of aging continuous degradation at time t, and fitting copula to the specific correlation. The developed reliability models are demonstrated for an application example of a micro‐electro‐mechanical system.     

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.     

A new combinatorial model for deterministic competing failure analysis

Components in many engineering and industrial systems can experience propagated failures, which not only cause the failure of the component itself but also affect other components, causing extensive damage to the entire system. However, in systems with functional dependence behavior where failure of a trigger component may cause other components (referred to as dependent components) to become unusable or inaccessible, failure propagation originating from a dependent component could be isolated if the corresponding trigger component fails first. Thus, a time-domain competition exists between the failure propagation effect and the failure isolation effect, which poses a great challenge to the system reliability modeling and analysis. In this work, a new combinatorial model called competing binary decision diagram (CBDD) is proposed for the reliability analysis of systems subject to the competing failure behavior. In particular, special Boolean algebra rules and logic manipulation rules are developed for system CBDD model generation. The corresponding evaluation algorithm for the constructed CBDD model is also proposed. The proposed CBDD modeling method has no limitation on the type of component time-to-failure distributions. A memory system example and a network example are provided to demonstrate the application of the proposed model and algorithms. Correctness of the proposed method is verified using the Markov method.     

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.     

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.     

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability‐related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression models have been modified in order to account for autocorrelated data. Then, a cumulative sum (CUSUM) control chart and an exponentially weighted moving average (EWMA) control chart based on conditional expected values have been proposed to monitor the quality variable with Weibull distribution while taking the effective covariates into consideration. Extensive simulation studies reveal that the CUSUM control chart outperforms its counterpart in detecting out‐of‐control conditions. Finally, a real case study in a textile industry has been provided to investigate the application of the CUSUM control scheme.     

Likelihood-based inference for a frailty-copula model based on competing risks failure time data

A competing risks phenomenon arises in industrial life tests, where multiple types of failure determine the working duration of a unit. To model dependence among marginal failure times, copula models and frailty models have been developed for competing risks failure time data. In this paper, we propose a frailty-copula model, which is a hybrid model including both a frailty term (for heterogeneity among units) and a copula function (for dependence between failure times). We focus on models that are useful to investigate the reliability of marginal failure times that are Weibull distributed. Furthermore, we develop likelihood-based inference methods based on competing risks data, including accelerated failure time models. We also develop a model-diagnostic procedure to assess the adequacy of the proposed model to a given dataset. Simulations are conducted to demonstrate the operational performance of the proposed methods, and a real dataset is analyzed for illustration. We make an R package “gammaGumbel” such that users can apply the suggested statistical methods to their data.     

Competing failure analysis considering cascading functional dependence and random failure propagation time

Functional dependence (FDEP) exists in many real‐world systems, where the failure of one component (trigger) causes other components (dependent components) within the same system to become isolated (inaccessible or unusable). The FDEP behavior complicates the system reliability analysis because it can cause competing failure effects in the time domain. Existing works have assumed noncascading FDEP, where each system component can be a trigger or a dependent component, but not both. However, in practical systems with hierarchical configurations, cascading FDEP takes place where a system component can play a dual role as both a trigger and a dependent component simultaneously. Such a component causes correlations among different FDEP groups, further complicating the system reliability analysis. Moreover, the existing works mostly assume that any failure propagation originating from a system component instantaneously takes effect, which is often not true in practical scenarios. In this work, we propose a new combinatorial method for the reliability analysis of competing systems subject to cascading FDEP and random failure propagation time. The method is hierarchical and flexible without limitations on the type of time‐to‐failure distributions for system components. A detailed case study is performed on a sensor system used in smart home applications to illustrate the proposed methodology.     

Reliability Allocation for Series-Parallel Systems subject to Potential Propagated Failures

To analyze the dependent failures in the early stage of system development, this paper considers the potential propagated failures in the reliability allocation process. Factors which can be used to not only measure the component importance but also to reflect the influence brought by propagated failures are proposed. Specifically, cooperative game theory is introduced to explore how the propagated failures affect the failure severity level. Failure rates are obtained by using the Alpha Factor Model with the consideration of dependence among components. Reliability improvement rate is also developed to proportionally assign the target improvement of system reliability to the corresponding components. Furthermore, reliability allocation frameworks for series, parallel and series-parallel systems are designed respectively to make the proposed model meet a wide range of applications. An illustrative example of a hydraulic cooling system is presented to show how the proposed approach is applied. The allocation results demonstrate that the proposed method can achieve a valid reliability improvement with the minimum error.     

Improving the Product Reliability in Multistage Manufacturing and Service Operations

Monitoring and improving the product reliability is of main concern in a large number of multistage manufacturing processes. The process output is commonly inspected under limited load conditions, and the tensile strength of reliability‐related quality characteristic is measured. This brings about censored observations that make the direct application of traditional control charts futile. The monitoring procedure becomes aggravated when the influence of variable competing risk is pronounced during the conducted test. To deal with this critical issue, we propose a regression‐adjusted cumulative sum (CUSUM) chart to effectively monitor a quality characteristic that may be right censored because of both fixed and variable competing risks. Moreover, two exponentially weighted moving average (EWMA) control charts on the basis of conditional expected values are devised to detect decreases in the tensile mean. The comparison of the three competing monitoring schemes confirms the superiority of the regression‐adjusted CUSUM procedure. Not only is the proposed control chart applicable to manufacturing processes with the aim of monitoring reliability‐related quality variables, it is also appropriate for monitoring similar quality measurements in service operations such as survivability measures in healthcare services. Copyright © 2011 John Wiley & Sons, Ltd.     

Reliability modeling of correlated competitions and dependent components with random failure propagation time

Function dependence takes place in systems where the malfunction of certain trigger component(s) causes other system components (referred to as dependent components) to become unusable or inaccessible. Systems undergoing function dependence often exhibit diverse statuses due to competitions in the time domain between propagated failure from a dependent component and local failure of the corresponding trigger component. If the former wins (ie, occurring first), a failure propagation effect is induced, crashing the entire system. If the latter wins, a failure isolation effect may be induced quarantining the damage from the propagated failure (the isolation effect can occur in a deterministic or probabilistic manner depending on applications). Existing models addressing such competitions have restrictive assumptions such as uncorrelated competitions from multiple function dependence groups, zero or negligible failure propagation time, and deterministic failure isolation effect. This paper advances the state of the art by proposing a combinatorial reliability model for systems undergoing correlated, probabilistic competitions and random failure propagation time for dependent components. A case study of a wireless body area network system for patient monitoring is performed to illustrate the proposed methodology and effects of different model parameters on the system reliability.     

Mathematical Model of the Operational Reliability of Intelligent Transducers

A mathematical model is formulated for the operational reliability of intelligent transducers used at the Astrakhan Gas Reprocessing Plant. The parameters are obtained of the theoretical distribution law for the duration of failure-free operation of the transducers, their reliability function, and their failure intensity.     

Reliability of systems subject to shocks with a stochastic dependence for the damages

We study a general reliability shock model that extends some classical shock models allowing a correlation structure for the variables involved in its definition. The model is governed by a sequence of random vectors of correlated variables: the intershock time, the magnitude and the damage caused by the shock. The distribution function of the failure time of the system and its mean value are provided. We conclude showing some applications of this kind of models.     

Statistical inference on accelerated life testing with dependent competing failure model under progressively type-II censored data based on copula theory

In this paper, we discuss the statistical analysis of a constant-stress accelerated dependent competing failure model under progressively type-II censoring based on a copula function. The dependence structure of lifetimes is constructed when the copula is a bivariate Clayton copula. The maximum likelihood estimations (MLEs) of the model parameters are derived. We also get the coverage probability of the 95% confidence intervals of the parameters based on MLEs and bootstrap confidence intervals. Finally, a real data set of some insulation system for electric motors was demonstrated for illustrative purpose.     

A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events

This paper deals with the condition-based maintenance of single-unit systems which are subject to the competing and dependent failures due deterioration and traumatic shock events. The main aim is to provide a model to assess the value of condition monitoring information for the maintenance decision-making. A condition-based periodic inspection/replacement policy is developed and compared with a benchmark time-based block replacement policy.Numerical results show that it is indeed useful to follow closely the actual evolution of the system to adapt the maintenance decisions to the true system state to improve the performance of maintenance policies. The analysis of the maintenance costs savings can be used to justify or not the choice to implement a policy based on condition monitoring information and to invest in condition monitoring devices.