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Redundant structure has been widely deployed to improve system reliability, as when one unit fails, the system can continue to function by using another one. Most existing studies rely on the similar assumption that the heterogeneous units are subject to periodic inspections and identical in terms of their aging situations and the numbers of resisted shocks. In practice, it is often adequate to trigger a unit individually in the event of a single shock, which intensifies the degradation of that unit, accordingly, requiring a sooner inspection to ensure its safety. In this study, the stochastic dependency among units is addressed firstly by introducing a novel activation sequence. Secondly, an adaptive system-level inspection policy is proposed by prioritizing the unit with a worse state. Finally, we take advantage of Monte Carlo methods to simulate the whole process and estimate two objectives, referring to the average system unavailability and maintenance cost, in a designed service time. It is found that the two objectives are contradictory through numerical examples. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, therefore, has been employed to find the optimal solutions in system unavailability and cost, which provide clues for practitioners in decision-making. • Modeling and quantifying the dependence of units in redundant structure caused by the activation sequence in withstanding random shocks. • Merging the unit-level inspection and system-level inspection into an adaptive and aperiodic testing policy. • Multi-objective optimization in seeking the optimal inspection and maintenance strategies. [ABSTRACT FROM AUTHOR]

In the context of equipment leasing, maintenance service is usually bundled with the leased equipment and offered by the lessor as an integrated package under a lease contract. The lessor is then responsible to prescribe an effective maintenance policy to keep the equipment operational in an economical way. This paper investigates upgrade and preventive maintenance (PM) strategies for industrial equipment during successive usage-based lease contracts with consideration of a warranty period, from the lessor's perspective. The accelerated failure time model and age reduction model are adopted to capture the effect of usage rate and imperfect PM/upgrade on the equipment reliability, respectively. More importantly, since equipment usage rates may vary across different lease contracts, this study develops an age correspondence framework to characterise usage rate shifts between successive lease periods. The optimal upgrade degree and the optimal number and level of PM actions are progressively updated for each upcoming lease period to minimise the total expected lease servicing cost, by considering the usage rate and maintenance implementation history. Numerical studies show that under given cost structures, periodical PM activities within each lease period tends to outperform the pre-leasing upgrade actions, though both of them can reduce the lease servicing cost. [ABSTRACT FROM AUTHOR]

In this paper, the compound Poisson phase-type process is defined and analyzed. This paper proves that for a non-negative compound Poisson phase-type process, the compound value for all the arrivals by a given time can be approximated by a phase-type distribution. As an application of this process, three different shock models are studied: the cumulative shock model, a degradation-threshold-shock model, and a shock model for the multi-component system. A novel approach is proposed to compute the system's reliability under the aforementioned shock models for a general counting process. Numerical illustrations are presented. [ABSTRACT FROM AUTHOR]

Warranty databases managed by most world-leading manufacturers are constantly expanding in the big data era. An important application of warranty databases is to detect unobservable reliability problems that emerge at design and/or manufacturing stages, through modeling and analysis of warranty claims data. Usually, serious reliability problems will result in certain abnormal patterns in warranty claims, which can be captured by appropriate statistical methods. In this article, a dynamic control charting scheme is developed for early detection of reliability problems by monitoring warranty claims one period after another, over the product life cycle. Instead of specifying a constant control limit, we determine the control limits progressively by considering stochastic product sales and non-homogeneous failure processes, simultaneously. The false alarm rate at each time period is controlled at a desired level, based on which abrupt changes in field reliability, if any, will be detected in a timely manner. Furthermore, a maximum-likelihood-based post-signal diagnosis scheme is presented to aid in identifying the most probable time of problem occurrence (i.e., change point). It is shown, through in-depth simulation studies and a real case study, that the proposed scheme is able to detect an underlying reliability problem promptly and meanwhile estimate the change point with an acceptable accuracy. Finally, a moving window approach concerning only recent production periods is introduced to extend the original model so as to mitigate the "inertia" problem. [ABSTRACT FROM AUTHOR]

This paper proposes a condition-based maintenance (CBM) policy for a deteriorating system whose state is monitored by a degraded sensor. In the literature of CBM, it is commonly assumed that inspection of system state is perfect or subject to measurement error. The health condition of the sensor, which is dedicated to inspect the system state, is completely ignored during system operation. However, due to the varying operation environment and aging effect, the sensor itself will suffer a degradation process and its performance deteriorates with time. In the presence of sensor degradation, the Kalman filter is employed in this paper to progressively estimate the system and the sensor state. Since the estimation of system state is subject to uncertainty, maintenance solely based on the estimated state will lead to a suboptimal solution. Instead, predictive reliability is used as a criterion for maintenance decision-making, which is able to incorporate the effect of estimation uncertainty. Preventive replacement is implemented when the estimated system reliability at inspection hits a specific threshold, which is obtained by minimizing the long-run maintenance cost rate. An example of wastewater treatment plant is used to illustrate the effectiveness of the proposed maintenance policy. It can be concluded through our research that: 1) disregarding the sensor degradation while it exists will significantly increase the maintenance cost and 2) the negative impact of sensor degradation can be diminished via proper inspection and filtering methods. Note to Practitioners—This paper was motivated by the observation of sensor degradation in wastewater treatment plants but the developed approach also applies to other systems such as manufacturing systems, chemical plants, and pharmaceutical factories, where sensors are dedicated to a long-time operation in a harsh environment. This paper investigates the impact of sensor degradation on CBM and suggests that the effect of sensor degradation should be carefully addressed while making maintenance decisions. Otherwise, it will lead to a suboptimal maintenance decision and increase the operating cost. An optimal maintenance decision, which contains the optimal inspection interval and reliability threshold, is achieved via minimizing the long-run cost rate. In the presence of measurement noise and intrinsic uncertainty from degradation, a stochastic filtering approach is employed to estimate the system and sensor state. Based on the estimated states and the calculated reliability, a dynamic maintenance decision is obtained at each inspection. This paper can be further extended considering non-Gaussian noise and alternative degradation processes. [ABSTRACT FROM AUTHOR]

Among recent system models, one specific type of system is generally used to model the dependence among components. Components are connected parallel in such systems as they fail one by one and are supposed to share the system work load. The model is thus referred to as the load‐sharing system model. Despite the availability of extensive reliability assessment methods for different systems, load‐sharing systems have not received enough attention from the scholars who have studied reliability assessment so far. Load‐sharing systems are generally designed for high levels of reliability. Therefore, tests for such systems can be expensive and time consuming. Limitation on resources always leads to small test sample sizes. This increases the difficulties associated with obtaining an accurate and robust system reliability assessment result. This paper proposes a novel assessment method for a certain type of load‐sharing system with components following exponential lifetime distributions. Based on the parameter estimation of the system reliability model, we introduce the Winterbottom‐Cornish‐Fisher asymptotic expansion method for implementing a correction of normal approximation. We demonstrate the accuracy of our method through a series of examples and simulation studies. [ABSTRACT FROM AUTHOR]

Reliability assessment of complex systems is an important yet difficult task. The difficulty arises largely because of heterogeneous component-level data, for example, lifetime data, degradation data, component-level assessment results, and prior information. A method for reliability assessment is developed in this article for systems plagued by degradation and lifetime data. Our framework divides component-level information into two types. These two types are then combined using a systematic approach. This article describes the method along with some application examples that demonstrate the approach capable of overcoming several difficulties associated with conventional reliability assessment approaches. [ABSTRACT FROM AUTHOR]

Conducting a Reliability Demonstration Test (RDT) is a crucial step in production. Products are tested under certain schemes to demonstrate whether their reliability indices reach pre-specified thresholds. Test schemes for RDT have been studied in different situations, e.g., lifetime testing, degradation testing and accelerated testing. Systems designed with several structures are also investigated in many RDT plans. Despite the availability of a range of test plans for different systems, RDT planning for load-sharing systems hasn’t yet received the attention it deserves. In this paper, we propose a demonstration method for two specific types of load-sharing systems with components subject to two distributions: exponential and Weibull. Based on the assumptions and interpretations made in several previous works on such load-sharing systems, we set the mean time to failure (MTTF) of the total system as the demonstration target. We represent the MTTF as a summation of mean time between successive component failures. Next, we introduce generalized test statistics for both the underlying distributions. Finally, RDT plans for the two types of systems are established on the basis of these test statistics. [ABSTRACT FROM AUTHOR]

In this paper, control charts for monitoring the exponential type-II censoring samples are investigated. Such data are very common in many practical inspection scenarios in reliability context when items are replaced in groups after a period of time. The average time to signal, which involves both the number and the time of samples inspected until a signal occurs, is a good criterion to evaluate the performance of control charts. We propose an average time to signal-unbiased control chart with known parameter and compare the proposed method with the traditional ones. The results indicate the proposed control chart is more sensitive to system deterioration. Then the effects of parameter estimation on the proposed control charts are evaluated. Because the control limits with estimated parameters result in more false alarms, an adjusted control chart with estimated parameters is proposed and the self-starting control chart based on a sequential sampling scheme is adopted to solve the phase I problem. Finally, two examples are given to illustrate the implementation of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Digital networked control systems are of growing importance in safety-critical systems and perform indispensable function in most complex systems today. Networked degradations such as transmission delay and packet dropout cause such systems to fail to satisfy performance requirements, and eventually affect the overall reliability. It is necessary to get a model to verify and evaluate the system reliability in early design phase, prior to its implementation. However, existing probabilistic models only provide partial descriptions of such coupled networks and control system. In this paper, a new stochastic model represented by linear discrete-time approach is proposed, considering data packet transmissions in both channels: controller-to-actuator and sensor-to-controller. Different from pervious works, the historical behaviors of networked degradations are modeled by multistate Markov chains with uncertainties, releasing the assumption that faults of all periods are independent of each other. The concept of domain requirements for such systems is considered here, contributing to the integration of control and reliability engineering. Methodologies for quantitatively assessing the reliability of the single- and sequential-control goal are derived from the Monte Carlo method. An example of an industrial heat exchanger digital networked control system is provided to illustrate the effectiveness of the model and method. [ABSTRACT FROM PUBLISHER]

This article presents certain new approaches to the reliability modeling of systems subject to shared loads. It is assumed that components in the system degrade continuously through an additive impact under load. The reliability assessment of such systems is often complicated by the fact that both the arriving load and the failure of components influence the degradation of the surviving components in a complex manner. The proposed approaches seek to ease this problem, by first deriving the time to prior failures and the arrival of random loads and then determining the number of failed components. Two separate models capable of analyzing system reliability as well as arriving at system maintenance and design decisions are proposed. The first considers a constant load and the other a cumulative load. A numerical example is presented to illustrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

The IG process models have been shown to be an important family in degradation analysis. In this paper, we are interested in optimal constant-stress accelerated degradation tests (ADTs) planning when the underlying degradation follows the inverse Gaussian (IG) process. We first consider ADT planning for the IG process without random effects. Asymptotic variance of the estimate of a lower quantile is derived, and the objective of the planning is to minimize this variance by properly choosing the testing stresses, and the number of samples allocated to each stress. Next, ADT planning for a random-effects IG process model is considered. We then applied the IG process to fit the stress relaxation data of a component, and use the developed methods to help with the optimal ADT design. [ABSTRACT FROM AUTHOR]

Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet easy-to-implement framework for analyzing progressively censored data by making use of the stochastic EM algorithm. On the basis of this framework, we develop specific stochastic EM procedures for several popular lifetime models. These procedures are shown to be very simple. We then demonstrate the applicability and efficiency of the stochastic EM algorithm by a fatigue life data set with proper modification and by a progressively censored data set from a life test on hard disk drives. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Availability/reliability is a main feature of design and operation of all engineering systems. Recently, availability evaluation of multi-state systems with different structures is at the center of attention due to the wide applications in engineering. In this paper, a dynamic model is developed for the availability assessment of multi-state weighted k-out-of-n systems. Then, in a design optimization problem, the availability and capacity for the components of such systems are optimized by genetic algorithm. In the dynamic model, the probabilities and capacities of components in different states are allowed to be changed over time. For availability assessment, universal generating function and Markov process are adopted. Application of the proposed model is illustrated using a real-world marine transportation system in order to evaluate and compare the presented optimization problems in assessing system availability. [Copyright &y& Elsevier]

This paper proposes a sequential failure limit maintenance policy for a repairable system. The objective system is assumed to have k+1 states, including one working state and k failure states, and the multiple failure states are classified potentially by features such as failure severity or failure cause. The system deteriorates over time and will be replaced upon the Nth failure. Corrective maintenance is performed immediately upon each of the first (N-1) failures. To avoid the costly failure, preventive maintenance actions will be performed as soon as the system's reliability drops to a critical threshold R. Both preventive maintenance and corrective maintenance are assumed to be imperfect. Increasing and decreasing geometric processes are introduced to characterize the efficiency of preventive maintenance and corrective maintenance. The objective is to derive an optimal maintenance policy (R^\ast,N^\ast) such that the long-run expected cost per unit time is minimized. The analytical expression of the cost rate function is derived, and the corresponding optimal maintenance policy can be determined numerically. A numerical example is given to illustrate the theoretical results and the maintaining procedure. The decision model shows its adaptability to different possible characteristics of the maintained system. [ABSTRACT FROM AUTHOR]

Abstract: The progressive increase in marine vessel transportation, in recent years, is often a cause of congestion at sea and main cause of highly irregular vessel’s travel time. This greatly affects scheduling of the facilities at the harbor and also related logistics. As a result, the reliability value of any marine vessel becomes a crucial factor in associated decision making. Modeling the uncertainty in estimation of marine vessel reliability has been a research interest for quite some time now. This paper investigates the problem in a different sense and tries to model the uncertainties using expert’s opinions and their imprecise responses. Marine vessel transportation reliability is viewed in an entirely different perspective and framework. This paper initially proposed a transportation reliability estimation procedure considering 12 decision variables divided into three stages. Two realistic models based on fuzzy sets are subsequently developed; with two scenarios for the first model. The first model utilizes fuzzy arithmetic; whereas the second one is based on rule bases. The paper demonstrates how information based on experience of the experts on marine vessels could be used to obtain its reliability value. Both the models would be helpful where imprecision is an intrinsic attribute of the accessible data in case of sea going vessels. [Copyright &y& Elsevier]

There are many real-life situations where exact failure times are not available or not easily available for reliability analysis. Motivated by this problem, this work is concerned with the analysis of reliability data without exact failure times. The unavailability of exact failure times has compelled us to develop a simplified version of the maximum likelihood (ML) estimation for reliability parameters and measures. The approach is applicable to single-parameter reliability models including the exponential reliability model and the Weibull reliability model with an assumed or known shape parameter value. In the latter case, it takes advantage of the fact that in many practical situations a reasonable estimate of the Weibull shape parameter is attainable by certain means. This is particularly valuable for reliability assessment of highly censored Weibull data with only a few failures, because in such situations it is highly desirable or necessary to exploit prior knowledge of the Weibull shape parameter to compensate for the limited information contained in the data in the hope of making a sound assessment. This is an application in a sense of the ideas and principles of the emerging paradigm of statistical engineering. The methods are developed by practitioners and for practitioners. We show that they offer practitioners a handy and efficient tool for reliability analysis, using hard disk drive product testing as examples. [ABSTRACT FROM AUTHOR]

Abstract: Reliability assessment for hard disk drives (HDDs) is important yet difficult for manufacturers. Motivated by the fact that the particle accumulation in the HDDs, which accounts for most HDD catastrophic failures, is contributed from the internal and external sources, a counting process with two arrival sources is proposed to model the particle cumulative process in HDDs. This model successfully explains the collapse of traditional ALT approaches for accelerated life test data. Parameter estimation and hypothesis tests for the model are developed and illustrated with real data from a HDD test. A simulation study is conducted to examine the accuracy of large sample normal approximations that are used to test existence of the internal and external sources. [Copyright &y& Elsevier]

This article considers optimal maintenance and allocation of elements in a Linear Multi-state Consecutively Connected System (LMCCS), which is important in signal transmission and other network systems. The system consists of N+1 linearly ordered positions (nodes) and fails if the first node (source) is not connected with the final node (sink). The reliability of an LMCCS has been studied in the past but has been restricted to the case when each system element has a constant reliability. In practice, system elements usually fail with increasing failure probability due to aging effects. Furthermore, in order to increase system availability, resources can be put into the maintenance of each element to increase the availability of the element. In this article, a framework is proposed to solve the cost optimal maintenance and allocation strategy of this type of system subject to an availability requirement. A universal generating function is used to estimate the availability of the system. A genetic algorithm is adopted for optimization. Illustrative examples are presented. [ABSTRACT FROM PUBLISHER]

The operation process or environment usually has a significant influence on system lifetime. In this paper, a lifetime optimization approach based on linear programming (LP) is proposed to maximize the transportation system lifetime, in which a semi-Markov (SM) model is used to model the operation process. In the proposed method, we first formulate the optimization problem as an LP model that is used to find the optimal transient probability of each state. Then, an analytical method is performed to obtain the corresponding optimal sojourn-time distribution parameters of the SM process. Finally, the proposed approach is applied to a port oil transportation system to show that it can efficiently ensure that the transportation system has a long lifetime. [ABSTRACT FROM PUBLISHER]

Exponential charts based on time-between-events (TBE) data were developed for monitoring high-yield process like the process which has achieved six-sigma quality level and has recently shown to be very useful in manufacturing systems, in reliability and maintenance monitoring, and also in service-related applications in general. This article develops an economic model of the exponential chart (known as TBE chart) for monitoring time-between-events data; the design algorithm considers the random characteristic of the process shifts and therefore better reflects the real process conditions. The probability distribution of the random process shift is modeled by a Rayleigh distribution based on the sample data acquired during the operation of the control chart. The design of the proposed control chart scheme is demonstrated, and the properties are compared with that of other exponential charts. The results of the numerical studies show that the consideration of the random process shift in designing an exponential chart provides an excellent in-control stability of the charting scheme, which in turn helps in saving time and cost for searching the false alarms. The proposed control chart is easy to understand and operate, and thus the floor operators can utilize and understand it as easily as with a traditional charting scheme. [ABSTRACT FROM AUTHOR]

Abstract: System reliability assessment is one of the major acts in the operation and maintenance of every industrial and service sector, which also holds true for maritime transportation system. The complexity of the maritime transportation system is a prime obstacle in the evaluation of the operational reliability of the system; mainly due to the fact that statistical data on the important parameters and variables is scarce. This makes the application of fuzzy sets and fuzzy logic a viable option to overcome the data problem with regards to imprecision or vagueness in parameters and variables values. In this paper, the different decisive factors, affecting maritime transportation systems, are modeled in the form of linguistic variables. Techniques such as aggregation, mapping of fuzzy sets using distance measure and fuzzy logic rule base are used to arrive at subjective operational reliability value. The complete procedure is demonstrated with an example. [Copyright &y& Elsevier]

There has been quite some research on the development of tools and techniques for grid systems, yet some important issues, e.g., grid service reliability and task scheduling in the grid, have not been sufficiently studied. For some grid services which have large subtasks requiring time-consuming computation, the reliability of grid service could be rather low. To resolve this problem, this paper introduces Local Node Fault Recovery (LNFR) mechanism into grid systems, and presents an in-depth study on grid service reliability modeling and analysis with this kind of fault recovery. To make LNFR mechanism practical, some constraints, i.e. the life times of subtasks, and the numbers of recoveries performed in grid nodes, are introduced; and grid service reliability models under these practical constraints are developed. Based on the proposed grid service reliability model, a multi-objective task scheduling optimization model is presented, and an ant colony optimization (ACO) algorithm is developed to solve it effectively. A numerical example is given to illustrate the influence of fault recovery on grid service reliability, and show a high efficiency of ACO in solving the grid task scheduling problem. [ABSTRACT FROM AUTHOR]

Abstract: Redundancy or standby is a technique that has been widely applied to improving system reliability and availability in system design. In most cases, components in standby system are assumed to be statistically identical and independent. However, in many practical applications, not all components in standby can be treated as identical because they have different failure and repair rates. In this paper, one kind of such systems with two categories of components is studied, which is named k-out-of-(M+N):G warm standby system. In the system, one category of the components is of type 1 and the other type 2. There are M type 1 components and N type 2 components. Components of type 1 have a lower failure rate and are preferably repaired if there is one failed. There are r repair facilities available. By using Markov model, the system state transition process can be clearly illustrated, and furthermore, the solutions of system availability and reliability are obtained based on this. An example representing a power-generator and transmission system is given to illustrate the solutions of the system availability and reliability. [Copyright &y& Elsevier]

Parallel redundancy is a common approach to increase system reliability and mean time to failure. When studying systems with redundant components, it is usually assumed that the components are independent; however, this assumption is seldom valid in practice. In the case of dependent components, the effectiveness of adding a component may be quite different from the case of independent components. In this paper we investigate how the degree of correlation affects the increase in the mean lifetime for parallel redundancy when the two components are positively quadrant dependent. A number of bivariate distributions that can be used in the modeling of dependent components are compared. Various bounds are also derived. The results are useful in reliability analysis as well as for designers who are required to take into account the possible dependence among the components. [ABSTRACT FROM AUTHOR]

Manufacturers usually want to predict the warranty cost for new products under affordable maintenance policies. With insufficient reliability information, experimental degradation tests are commonly conducted to predict the field reliability before the products are put on the market. In this paper, we propose a novel warranty cost optimization framework based on degradation data within a finite warranty period under the assumption of imperfect repairs. The expected number of warranty claims is given in the analytical form. Two sources of uncertainty are considered to estimate the field reliability for more realistic warranty cost prediction: the uncertainty in experimental data and the variation in field conditions. Effects of imperfect repairs are assumed to be random. The warranty cost for a single repair is assumed to be associated with the improvement factor of imperfect repairs. Optimal imperfect repair policy is obtained by minimizing the expected warranty cost for each sold product. Further, the proposed framework can facilitate the interval prediction for warranty cost. Numerical results show that the proposed analytical method to evaluate warranty claims significantly outperforms simulation methods from the perspective of computational efforts. Finally, an application example of degradation tests along with sensitivity analysis is presented to illustrate the proposed framework. [ABSTRACT FROM AUTHOR]

For power system decision makers, they should decide a reasonable wind capacity or target penetration level considering the balance of wind curtailment, investment, system reliability and social benefit. Assessing wind curtailment under different wind capacity, considering the wind resource uncertainty, is therefore necessary for utilizing wind power effectively. In this paper, fuzzy wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources based on the wind speed distribution. By investigating the maximal wind power output that can be accepted in candidate system nodes, a fuzzy linear programming problem is proposed to decide the required wind capacity considering the fuzzy wind capacity factor. Based on the cumulative distribution function of wind capacity resulting from large number of simulations, the relationship between wind capacity and wind curtailment is determined. The effectiveness of the proposed model is validated by its application in two power grids. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty of wind resources and assessing wind curtailment under different wind capacity. Compared with previous research, this method can aid power system planners to allocate wind capacity considering the possibilistic uncertainty of wind resources. [ABSTRACT FROM AUTHOR]

A dynamic preventive maintenance policy for system with continuously degrading components is investigated in this paper. Different from traditional cost-centric preventive maintenance policy, our maintenance strategy is formulated from the value perspective. Component value is modelled as a function of component reliability distribution. Maintenance action is triggered whenever the system reliability drops below a certain threshold. Our policy mainly consists of two steps: (i) determine which component to maintain; (ii) determine to what degree the component should be maintained. In Step 1, we introduce the yield-cost importance to select the most important component. In Step 2, the optimal maintenance level is obtained by maximizing the net value of the maintenance action. Finally, numerical examples are given to illustrate the proposed policy. [ABSTRACT FROM AUTHOR]

Abstract: Complex systems contain both hardware and software, and software reliability becomes more and more essential in system reliability context. In recent years, data-driven software reliability models (DDSRMs) with multiple-delayed-input single-output (MDISO) architecture have been proposed and studied. For these models, the software failure process is viewed as a time series and it is assumed that a software failure is strongly correlated with the most recent failures. In reality, this assumption may not be valid and hence the model performance would be affected. In this paper, we propose a generic DDSRM with MDISO architecture by relaxing this unrealistic assumption. The proposed model can cater for various failure correlations and existing DDSRMs are special cases of the proposed model. A hybrid genetic algorithm (GA)-based algorithm is developed which adopts the model mining technique to discover the correlation of failures and to obtain optimal model parameters. Numerical examples are presented and results reveal that the proposed model outperforms existing DDSRMs. [Copyright &y& Elsevier]

Abstract: Adaptive genetic algorithms (GAs) have been shown to be able to improve GA performance in reliability-related optimization studies. However, there are different ways to implement adaptive GAs, some of which are even in conflict with each other. In this study, a simple parameter-adjusting method using mean and variance of each generation is introduced. This method is used to compare two of such conflicting adaptive GA methods: GAs with increasing mutation rate and decreasing crossover rate and GAs with decreasing mutation rate and increasing crossover rate. The illustrative examples indicate that adaptive GAs with decreasing mutation rate and increasing crossover rate finally yield better results. Furthermore, a population disturbance method is proposed to avoid local optimum solutions. This idea is similar to exotic migration to a tribal society. To solve the problem of large solution space, a variable roughening method is also embedded into GA. Two case studies are presented to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Based on the fretting test system, the contact resistance of contact pairs with gold-plated PCB sample and different metal probe, under the different fretting conditions are investigated to evaluate the fretting characteristics. The environmental reliability assessment of the contact pairs after fretting is carried out through the thermal humidity experiment and salt spray experiment. The influence of environment on contacts reliability is evaluated by measuring and comparing the static contact resistance in the fretting region before and after the accelerated environmental experiment. It is found that the contact pair of silver-plated probe with thickness 4μm mating with gold-plated PCB sample has the best fretting characteristics and environmental reliability. • Based on the fretting test system, the contact resistance of contact pairs with gold-plated PCB sample and different metal probe, under the different fretting conditions are investigated to evaluate the fretting characteristics. • The influence of environment on contacts reliability is evaluated by measuring and comparing the static contact resistance in the fretting region before and after the accelerated environmental experiment. • It is found that the contact pair of silver-plated probe with thickness 4μm mating with gold-plated PCB sample has the best fretting characteristics and environmental reliability. [ABSTRACT FROM AUTHOR]

Abstract: The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor” and “information fusion factor”, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown. [Copyright &y& Elsevier]