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Dynamic fault tree (DFT) is a reliable method for modeling complex systems with dynamic characteristics. However, evaluating the reliability of such systems can be complex. The sequential binary decision diagram (SBDD) method simplifies this process by reducing computational complexity. This study investigates factors influencing SBDD size and proposes a new method, the complete priority ordering method, for ordering basic events in SBDDs. Results demonstrate the method's effectiveness in comparison to existing approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an active learning Kriging (ALK) based reliability analysis method for a multi‐output structural system by using a multiple response Gaussian process (MRGP) model. Firstly, various failure modes, including their interactions, are involved in a multi‐output structural system. The MRGP model is used to construct the surrogate model directly because it can efficiently characterize the correlation between different failure modes. The particle swarm optimization (PSO) algorithm is integrated into the MRGP model to optimize the hyperparameter. Secondly, similar to ALK‐based reliability method, three improved functions for these common learning functions (e.g., U‐function, EFF‐function, H‐function) are proposed, which consider the distance requirement between the iteration sample point and training samples. Finally, the cross‐validation methodology is employed as the stopping criterion and several numerical examples are provided to illustrate the effectiveness. [ABSTRACT FROM AUTHOR]

The multi‐output structural system with implicit function widely exists in actual engineering, which refers that the multiple output responses of structural system can be obtained by one experiment or finite element simulation. Considering the correlation of multiple output responses and the small failure probability involved in multi‐output structural system, this article proposes a novel active learning Kriging (ALK) based reliability method for multi‐output structural system by combining multiple response Gaussian process (MRGP) and importance sampling (IS). First, due to the Kriging model can only construct the surrogate model under the single‐output variable, the MRGP model is introduced to substitute the Kriging model and thus the correlation in multiple output responses can be efficiently described by a correlation matrix in MRGP model. Second, for the case that the distance information of new iterated sample point is not considered by the commonly used learning functions (U‐function, EFF‐function and H‐function) in ALK, three improved learning functions are correspondingly proposed. Finally, aiming at the problem that the small failure probability leads to the increasing of candidate sample pool and further results in low computational efficiency, the IS method is combined with the MRGP model to efficiently accomplish the reliability analysis for multi‐output structural system. Several examples are also provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The power law process (PLP) is widely used to analyze the failures of repairable systems, and the PLP parameter estimation is the primary concern for reliability assessment or maintenance decision making. Although the Bayesian estimation of the PLP has been studied in existing research, little attention has been paid to how to obtain its prior distribution, especially when the prior information is coming from multiple sources. To address this problem, a framework for Bayesian prior information fusion using evidence theory is proposed in this paper. This framework first uses evidence theory to represent the prior information from multiple sources or experts and then combines them into fused information. Based on the belief and plausibility functions of the fused information, the prior distribution is bounded by an upper and lower probability density functions which are derived by moment equivalence. A case study is also carried out to verify and illustrate the proposed method. The results show that this proposed approach is beneficial for the Bayesian estimation of the power law process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to its high efficiency and low power consumption, switched‐mode power supply (SMPS) represents the development trend of the stabilized voltage power supply. However, tolerance has become one of the key factors in the design of SMPS because of the process fluctuation of electronic components, unstable input parameters of the circuit system, influence of working conditions and environment, and the effect of aging. In order to improve the reliability of SMPS and reduce the manufacturing cost, this paper proposes a reliability analysis and optimization design method based on the tolerance and sensitivity analysis. Finally, this method is applied to the tolerance design for the positive switching power supply of the SMPS circuits, and the optimal tolerance design scheme is obtained. Furthermore, the reliability and probability density curves are evaluated. [ABSTRACT FROM AUTHOR]

Highlights • A reliability model for PMS subject to random shocks is proposed. • MRGP is used to deal with the dynamic non-exponential components. • A MC simulation procedure is proposed to evaluate PMS subject to random shocks. • The result confirms the importance of considering random shocks in PMS reliability. Abstract Phased-mission systems (PMSs) are widely used, especially in the aerospace industry. As in the outer space there are many kinds of cosmic rays, such as the Galactic Cosmic Rays (GCR), randomly hitting on these systems and causing significant impact on the electronics inside or outside the equipment, a reliability model for PMSs considering both finite and infinite random shocks is proposed in this paper. The modularization method is used to simplify the state space model for each phase and reduce the amount of system states, and the Markov regenerative process (MRGP) is used to describe the hybrid components' lifetime distributions and the dynamic behaviors within the modules. Then, two kinds of scenarios, finite and infinite random shocks effect, are both integrated into the dynamic modules. For demonstration, a phased altitude and orbit control system (AOCS) subjected to infinite random shocks is illustrated to demonstrate the procedure of the proposed Monte Carlo simulation. Thirdly, the evaluated system reliability under infinite random shocks is compared with the same system without considering random shocks. At last, a sensitivity analysis is also provided for completion. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Not only quantities but types of cycles were considered for turbine blades reliability prediction. • Fuzzy theory was introduced to represent uncertainties in all parts of reliability prediction. • Reliability prediction under fuzzy stress with and without fuzzy strength were conducted. Abstract Fatigue and fracture of turbine blades are fatal to aero engines. Reliability prediction of aero engines is indispensable to guarantee their safety. For turbine blades of aero engines, most recent research works only focus on the number of cycles and excavate information from a single source. To remove these limitations, a Physics of failure-based reliability prediction method using multi-source information fusion has been developed in this paper to predict the reliability of turbine blades of aero engines. In the proposed method, the fuzzy theory is employed to represent uncertainties involved in prediction. Case studies of reliability prediction under fuzzy stress with and without fuzzy strength are conducted by using a dynamic stress-strength interference model which takes types of cycles of aero engines into consideration. Results indicate that the proposed method is better in line with engineering practice and more flexible in decision making and it can predict the reliability of aero engine turbine blades to be an interval by utilizing the proposed linear fusion algorithm. In addition, the predicted interval contains results that are predicted by other commonly used information fusion methods Hence, the proposed method conduces to remove confusion made by selection of multiple methods. [ABSTRACT FROM AUTHOR]

Field reliability assessment and prediction is critical for the estimation, operation and health management of CNC machine tools. The classical methods for field reliability of CNC Machine Tools assessment and prediction are challenged with the issues of expensive reliability tests, small sample size and unit non-homogeneity. In order to solve these problems, this paper introduces a degradation analysis based reliability assessment method for CNC machine tools under performance testing. Since the degradation is an independent increment process, the gamma process is employed to characterize the degradation process of CNC machine tools. The random effects are introduced to accommodate performance degradation model with unit non-homogeneity. The parameters of model are updated by Bayesian estimation approach. As a case study, the CNC Machine Tools is studied to illustrate the approach. And the proposed method is demonstrated precise for practical use. [ABSTRACT FROM AUTHOR]

With the increasing complexity and size of modern advanced engineering systems, the traditional reliability theory cannot characterize and quantify the complex characteristics of complex systems, such as multi-state properties, epistemic uncertainties, common cause failures (CCFs). This paper focuses on the reliability analysis of complex multi-state system (MSS) with epistemic uncertainty and CCFs. Based on the Bayesian network (BN) method for reliability analysis of MSS, the Dempster-Shafer (DS) evidence theory is used to express the epistemic uncertainty in system through the state space reconstruction of MSS, and an uncertain state used to express the epistemic uncertainty is introduced in the new state space. The integration of evidence theory with BN which called evidential network (EN) is achieved by adapting and updating the conditional probability tables (CPTs) into conditional mass tables (CMTs). When multiple CCF groups (CCFGs) are considered in complex redundant system, a modified β factor parametric model is introduced to model the CCF in system. An EN method is proposed for the reliability analysis and evaluation of complex MSSs in this paper. The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability. The presented method has high computational efficiency, and the computational accuracy is also verified. [ABSTRACT FROM AUTHOR]

Degradation analysis is critical to reliability assessment and operational management of complex systems. Two types of assumptions are often adopted for degradation analysis: (1) single degradation indicator and (2) constant external factors. However, modern complex systems are generally characterized as multiple functional and suffered from multiple failure modes due to dynamic operating conditions. In this paper, Bayesian degradation analysis of complex systems with multiple degradation indicators under dynamic conditions is investigated. Three practical engineering-driven issues are addressed: (1) to model various combinations of degradation indicators, a generalized multivariate hybrid degradation process model is proposed, which subsumes both monotonic and non-monotonic degradation processes models as special cases, (2) to study effects of external factors, two types of dynamic covariates are incorporated jointly, which include both environmental conditions and operating profiles, and (3) to facilitate degradation based reliability analysis, a serial of Bayesian strategy is constructed, which covers parameter estimation, factor-related degradation prediction, and unit-specific remaining useful life assessment. Finally, degradation analysis of a type of heavy machine tools is presented to demonstrate the application and performance of the proposed method. A comparison of the proposed model with a traditional model is studied as well in the example. [ABSTRACT FROM AUTHOR]

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems. [ABSTRACT FROM AUTHOR]

Modern engineering systems are generally composed of multicomponents and are characterized as multifunctional. Condition monitoring and health management of these systems often confronts the difficulty of degradation analysis with multiple performance characteristics. Degradation observations generally exhibit an s-dependent nature and sometimes experience incomplete measurements. These issues necessitate investigating multiple s-dependent degradations analysis with incomplete observations. In this paper, a new type of bivariate degradation model based on inverse Gaussian processes and copulas is proposed. A two-stage Bayesian method is introduced to implement parameter estimation for the bivariate degradation model by treating the degradation processes and copula function separately. Degradation inferences for missing observation points, and for future observation points are investigated. A simulation study is presented to study the effectiveness of the dependence modeling and degradation inference of the proposed method. For demonstration, a bivariate degradation analysis of positioning accuracy and output power of heavy machine tools subject to incomplete measurements is provided. [ABSTRACT FROM AUTHOR]

This article is to study the similarities and differences in terms of computational methodologies in different nations, as well as to analyze the accuracy and reliability of them, and consequently to raise proposals to improve this methodology in China. The author chose an atrium-style metro station as a study case. Computations and comparisons were carried out on the evacuation time, according to the terms in Chinese “CFDOM” (GB50157-2013), the U.S. “NPFA130” (2014) and Japanese “JIS Railway Standards”. The results indicate that, the computational methodology in China is readily to be comprehended, while it ignores some important features. A certain extent of irrationality can be found in this methodology. While the American and Japanese codes specify the evacuation path in details, which leads to a more reasonable evacuation time. Therefore, in practical use, a synthetic methodology that considers multinational codes is commonly suggested, for the most precise evacuation time. [ABSTRACT FROM AUTHOR]

Traditionally, degradation testing and condition monitoring are used separately to investigate field reliability. Barriers are naturally formed between these two types of methods due to condition-discrepancies between lab testing and field monitoring, as well as time-varying missions among product population groups. In this paper, a joint framework for field reliability analysis is presented by integrating degradation testing data as well as mission operating information with condition monitoring observations. A coherent modeling strategy is introduced for the information integration by gradually adopting random effects, dynamic covariates, and marker processes into a baseline stochastic degradation model. In detail, random effects are introduced to cope with the inherent unit-to-unit variation. Dynamic covariates are adopted to deal with the external condition heterogeneity. Marker processes are used to account for the time-varying missions. To facilitate information integration and reliability analysis, the Bayesian method is used to implement parameter estimation and degradation analysis. The reliability assessment of products' populations, degradation prediction, and residual life prediction of individual products are investigated. Finally, an illustrative example for field degradation analysis of oil debris in a lubrication system of a machine tool's spindle system is presented. The effectiveness of information integration and the capability of degradation inference are demonstrated through this example. [ABSTRACT FROM PUBLISHER]

Traditional time-based reliability assessment methods evaluate the reliability of a multi-state system (MSS) from a population or a statistical perspective that the reliability of a system is computed purely based upon historical time-to-failure data collected from a large population of identical components or systems. These methods, however, fail to characterize the stochastic behaviors of a specific individual system. In this paper, by utilizing system-level observation history, a dynamic reliability assessment method for MSSs is put forth. The proposed recursive Bayesian formula is able to dynamically update the reliability function of a specific MSS over time by incorporating system-level inspection data. The dynamic reliability function, state probabilities, and remaining useful life distribution of an MSS in residual lifetime are derived for two common cases: the degradation of components follows a homogeneous continuous time Markov process, and a non-homogeneous continuous time Markov process. The effectiveness and accuracy of the proposed method are demonstrated via two numerical examples. [ABSTRACT FROM PUBLISHER]

This paper develops a Bayesian framework to assess the reliability and performance of multi-state systems (MSSs). An MSS consists of multiple multi-state components of which the degradation follows a Markov process. Due to the lack of sufficient data, and only vague knowledge from experts, the transition intensities of multi-state components between any pair of states and the state probabilities cannot be precisely estimated. The proposed Bayesian method can merge prior knowledge from experts' judgments with continuous and discontinuous inspection data to obtain posterior distributions of transition intensities. A simulation method embedded with the universal generating function (UGF) is developed to estimate the posterior state probabilities, the reliability, and the performance of the entire MSS. Two numerical experiments are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

• A new single-loop strategy for time-variant system reliability analysis is proposed. • The multiple response Gaussian process is adopted to depict the correlation between time-variant limit state functions. • The subset simulation is introduced into the proposed single-loop strategy to estimate the small failure probability. • The updated strategy and stopping criterion for the proposed single-loop time-variant reliability analysis are provided. This paper proposes a time-variant system reliability analysis method by combining multiple response Gaussian process (MRGP) and subset simulation (SS) to solve the small failure probability problem. One common method for time-variant reliability analysis is based on the double-loop procedure where the inner loop is the optimization for extreme values and the outer loop is extreme-value-based reliability analysis. In this paper, a new single-loop strategy is firstly proposed to decouple the double-loop procedure by using the best value in current initial samples to approximate the extreme value, thus the extremal optimization in inner loop can be avoided. Then the MRGP model is used to construct the surrogate model of extreme value response surface for time-variant system reliability analysis based on the approximated extremums. Meanwhile, the Kriging model is also constructed based on the initial samples to assist in searching the new sample point. Furthermore, for selecting the new point that resides as close to the extreme value response surface as possible from the Monte Carlo simulation (MCS) sample pool, three learning functions (U-function, EFF-function and H-function) are respectively used to find the new random variable sample point based on the MRGP model and the expected improvement (EI) function is used to find the new time sample point based on the Kriging model. Finally, for reducing the size of candidate sample pool and the computing burden, the SS method is combined with the MRGP model to deal with the small failure probability problem. The effectiveness of the proposed method is also demonstrated by several examples. [ABSTRACT FROM AUTHOR]

• This paper entitled "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model" has three highlights: • A time-variant reliability analysis with multiple failure modes is performed for industrial robot RV reducer using Kriging model. • The multiple response Gaussian process is adopted to depict the correlation of multiple time-variant limit state functions. • Three learning functions are used to choose the new point and the effectiveness is also testified. This paper proposes a time-variant reliability method for an industrial robot rotate vector (RV) reducer with multiple failure modes using a Kriging model. Firstly, the limit state functions of the industrial robot RV reducer are built by considering time-variant load and material degradation based on the failure physic method. Secondly, a time-variant reliability analysis method for multiple failure modes is proposed based on a double-loop Kriging model. The inner loop is the extremal optimization for each limit state function based on the efficient global optimization (EGO). The outer loop is the active learning reliability analysis by combining multiple response Gaussian process model (MRGP) and the Monte Carlo simulation (MCS). Furthermore, three learning functions (U-function, EFF-function and H-function) are individually adopted to choose a new sample point until the convergence is satisfied. Case studies are finally provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• The mixed redundancy strategy is introduced to the RAP of phased mission system. • The Semi-Markov process is used to evaluate the non-exponential PMS. • An optimization procedure based on the Genetic Algorithm is given. • A spacecraft propulsion system is used to illustrate the proposed method. The redundancy allocation problem (RAP) has been widely studied, which aims to identify the optimal design to achieve the best system indicators, such as system reliability or availability, under certain constraints. This problem has been studied well in the single phased mission systems. But many practical systems, like the manmade satellites or spacecraft, perform a series of tasks in multiple, consecutive, non-overlapping time durations (phases). For these multi-phased systems, dependencies among the phases need to be fully considered. To address this problem, the RAP of the phased mission systems (PMSs) is studied in this paper. Moreover, in order to improve system reliability as much as possible, the mixed redundancy strategy where both active and cold standby components can be simultaneously used in a subsystem is applied. The non-exponential components, which are more practical, are also considered. The Semi-Markov process (SMP) as well as a numerical approximation method are used to deal with the dynamic non-exponential components. Then, an improved Genetic algorithm (GA) is used to determine the types and quantities of active and cold-standby components in each subsystem to optimize the whole system. At last, the propulsion system in a spacecraft is optimized to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

• A novel single-loop procedure is proposed for time-variant reliability analysis. • Two commonly used learning functions and a new learning function are adopted to find the new point, respectively. • The comparison to Monte Carlo simulation indicates high accuracy and efficiency of the proposed method. This paper proposes a novel single-loop procedure for time-variant reliability analysis based on a Kriging model. A new strategy is presented to decouple the double-loop Kriging model for time-variant reliability analysis, in which the extreme value response in double-loop procedure is replaced by the best value in the current sampled points to avoid the inner optimization loop. Consequently, the extreme value response surface for time-variant reliability analysis can be directly established through a single-loop Kriging surrogate model. To further improve the accuracy of the proposed Kriging model, two methods are provided to adaptively choose a new sample point for updating the model. One method is to apply two commonly used learning functions to select the new sample point that resides as close to the extreme value response surface as possible, and the other is to apply a new learning function to select the new point. Synchronously, the corresponding different stopping criteria are also provided. It is worth nothing that the proposed single-loop Kriging model for time-variant reliability analysis is for a single time-variant performance function. To verify the proposed method, it is applied to four examples, two of which have with random process and others have not. Other popular methods for time-variant reliability analysis including the existing single-loop Kriging model are also used for the comparative analysis and their results testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Phased mission systems (PMSs) like satellites and spacecraft perform their functions over non-overlapping mission periods, called phases. One of the challenges in assessing reliability of PMSs comes from considering the s -dependence among phases, and the consideration on the multi-state behavior of components and systems makes the reliability analysis even more difficult. To effectively address this problem, a multi-state multivalued decision diagram algorithm for PMS and a multi-state multi-valued decision diagram model for phased mission system (PMS-MMDD) method is developed for the reliability modelling of non-repairable multi-state components. Based on the Semi-Markov process, a Markov renewal equation-based method is developed to deal with non-exponential multi-state components and a numerical method, the trapezoidal integration method, is used to compute the complex integrals in the path probability evaluation. A case study of a multi-state attitude and orbit control system in a spacecraft is analyzed to illustrate the proposed PMS-MMDD model and the Markov renewal equation-based evaluation method. The accuracy and computation efficiency of the proposed method are verified by the Monte Carlo simulation method. [ABSTRACT FROM AUTHOR]

• Detailed definition of PPMS (Periodic PMS) for space products. • Approximating model, errors and sensitivity analysis for electronics in PPMS. • Convenient reliability modeling for a power converter considering PPMS. • Optimization for redundancy design. The reliability of the power converter system, an essential energy adapter connecting solar panels and batteries in the satellite, is crucial to an entire satellite. In practical engineering, the reliability of electronic component or module of the power converter system is always calculated by applying the constant failure rate model, against the feature of a periodic phased mission system (PPMS) in space. Therefore, this paper adopts a new fusing failure rate to build a more accurate model of reliability considering PPMS. In particular, the applicability of the new model is demonstrated to not only components following exponential distribution, but also to others following Weibull distribution. Furthermore, for the converter level, the Dynamic Fault Tree and Markov Process (MP) are utilized to model converter's reliability with the help of the state lumping method. In the case study, the reliability modeling of a dual Buck-Boost converter in satellite is conducted, as well as the optimization for redundancy design. The result indicates that the reliability of the converter in the satellite is more accurate and reasonable than that of using traditional methods. [ABSTRACT FROM AUTHOR]

• The common cause failures is modeled and quantified by decomposed partial α factors; • Mixed uncertainties are quantified and expressed by the D-S evidence theory; • A hierarchical structure of system reliability is constructed by adding decomposed CCF events layer in evidential network; • Importance and imprecise sensitivities analysis methods are extended and developed; • The proposed method is effectively used to analyze the reliability of an auxiliary power supply system of a train. Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

• A Markov regenerative process model is developed to model the PMS under mixed random shocks. • A Monte Carlo simulation procedure is given to validate the proposed method. • The altitude and orbit control system is used to illustrate the proposed method. • The result analysis shows the influence of different shocks parameters. Recently, phased mission systems (PMSs) have been widely studied due to their wide range of applications, such as man-made satellites or spacecraft. The lifetime of the PMSs can be separated into several phases, in which their tasks, system configurations, and failure criteria could be different. Meanwhile, the randomly occurred shocks, such as the space radiation from outer space, will cause additional wear and fatal damage on the electronic devices in these systems, which will obviously affect the system reliability. To consider these shocks' effect in the reliability modeling of the PMSs, a Markov regenerative process (MRGP) based model is proposed in this paper. Firstly, shock models for components and the basic conceptions of MRGP are introduced. Then, a simple cold standby system is used to show the proposed MRGP model for systems under mixed shocks. And a Monte Carlo simulation procedure is applied to verify the results. At last, by integrating the proposed MRGP model and the modular method for PMSs, the reliability of an AOCS in a spacecraft is assessed to show the proposed method. [ABSTRACT FROM AUTHOR]

• Possible CCF scenarios are modeled and quantified by decomposed partial α factors. • Uncertainties for CCF events are reduced by hierarchical Bayesian inference method. • Reliability of redundant uncertain system with CCFs is modeled by survival signature. • Importance of components and CCF events are defined and ranked. • The proposed method is effectively used to analyze the reliability of a satellite subsystem. Redundant design has become the commonly used technique for ensuring the reliability of complex systems, which calls for great concern to common cause failure problems in such systems. Incomplete data in combination with vague judgments from experts introduce imprecision and epistemic uncertainties in the performance characterization of components. These issues need to be taken into account for assessing the system reliability. In this paper, a comprehensive reliability assessment method is presented by adopting the concept of survival signature to estimate the reliability of complex systems with multiple types of components. Particular attention is devoted to common cause failures (CCFs), which are modeled and quantified by decomposed partial α-decomposition method. Uncertainties caused by incomplete data for CCF events are reduced by hierarchical Bayesian inference. The component importance measure is enhanced to assess the importance of various possible CCF scenarios and to identify their potential impact on system reliability. The presented method is used to analyze the reliability of a dual-axis pointing mechanism for communication satellite, which is a commonly used satellite antenna control mechanism. The engineering application demonstrates the effectiveness of the method. [ABSTRACT FROM AUTHOR]

• A novel robust reliability growth planning decision method is proposed. • The robustness is quantified by the maximum amount of uncertainty. • A case study is applied to demonstrate the proposed approach. • This method is useful when sparse information is available. Resources allocation is one of the key issues in the planning of reliability growth testing. Sparse information and testing prototypes are available for program managers in the research and development phases, especially at the early development stage. However, program managers or engineers are often needed to make decisions with incomplete information or even severe uncertainty. Optimizing strategy has been used to allocate resources for reliability growth testing, which sets up optimization models and maximizes the reliability within the resource constraints. A novel robust decision method for the planning of reliability growth testing based on the information gap decision theory is proposed, which aims to satisfy the system reliability requirements and keeps the decision insensitive to the initial estimation of relevant uncertain parameters. The information gap robustness function provides an alternative approach to address the planning of reliability growth testing. The case study demonstrates the applicability of the proposed method in practical problems. The main advantage of this method is that only a few information of the uncertain parameters is required. The results indicate that this method is useful for program managers and reliability practitioners who are engaged in reliability growth planning. [ABSTRACT FROM AUTHOR]

Highlights • A generalized dynamic reliability model based on SSI theory is proposed for calculating the system reliability under complex load. • Gauss–Legendre quadrature formula is used to calculate the system reliability with a high accuracy. • Applicability and accuracy of the proposed method have been validated by a case. Abstract The traditional stress-strength interference (SSI) model regards the strength and the stress as two continuous random variables, but in practical engineering, the strength may be a stochastic degradation process. Besides continuous working load, a mechanical system often suffers from shock loads as well. How to calculate the dynamical reliability under complex load is a challenge that needs to be resolved. This paper proposes a generalized dynamic reliability model for the calculation of system reliability under complex load. The proposed model is available for system reliability problems under deterministic strength degradation or stochastic strength degradation processes. Six sigma and Gauss-Legendre quadrature formula are adopted to calculate the system reliability. A case study under three different conditions is presented to illustrate the application of the proposed model. The accuracy of the proposed method is compared with MCS. [ABSTRACT FROM AUTHOR]