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The fatigue damage of gear is caused by the coexistence and mutual influence of residual strength degradation and cumulative damage. The residual strength decreases continuously with the change of the loads and the increase of stress cycles. Most of the traditional cumulative damage models ignore the effect of residual strength degradation on cumulative damage. The exponential parameter in the traditional Corten‐Dolan model was defined as exponentials function related to the strength degradation coefficient, and the strength degradation coefficient was deduced by analyzing the residual strength degradation model, and the modified Corten‐Dolan model considering the residual strength degradation was established. Based on the stress‐strength interference model, a gear reliability model considering residual strength degradation was established. The effectiveness of the strength degradation model and the accuracy of the modified Corten‐Dolan model are verified by the analysis of the test data, which provides a new idea for the reliability analysis of gear. The accuracy of the gear reliability model proposed in this paper was verified by Monte‐Carlo (M‐C) simulation, which has practical significance for engineering application. [ABSTRACT FROM AUTHOR]

The input of low-quality feedstocks triggers the interdependence between workpiece quality and machine reliability, which will further adversely impact the performance of manufacturing systems. Considering the interconnected manufacturing system structures, our primary goal is to provide an effective method to compute the performance of networked manufacturing systems suffering from machine and low-quality feedstock interdependence. The strength of our work first lies in the model for the compound degradation process of machines and dissemination of low-quality feedstocks, which enables us to construct a response chain to model the interdependence between machines and feedstocks in the manufacturing network. Then, the second strength is the effective algorithm for the computation of route connectivity and quality loss of a manufacturing network based on the interdependence model. A computational experiment shows our models and algorithm can work well for evaluating the operational performance of manufacturing networks. [ABSTRACT FROM AUTHOR]

Fierce global competition drives automated manufacturing systems (AMSs) to be increasingly complex, which poses significant challenges on performance analysis and production control. The multistage production via serial stations will lead to the propagation of failures in AMSs, which will affect system performance by triggering complex competitions among multiple failure modes. Although machine performance and product quality have been considered, very little has been done to investigate the effect of imperfect quality inspection on competing failures. Focusing on a time balance serial AMS, this article presents a new competing failure model to investigate the complex interactions among machine failures, product quality, and inspection process, which enables the characterizations of time-delayed propagation of failure, accumulation of degradation, and dynamics of states in serial AMSs. In order to further analyze the impact of competing behaviors on system performance, we have also developed decision diagram models and algorithms, which are evaluated and validated on serial AMSs with imperfect inspection, revealing the characteristic of multistate interactions. Experimental results show that the proposed methods have strong potentials for performance modeling and analysis of serial AMSs and also demonstrate general applicability for manufacturing decision making. [ABSTRACT FROM AUTHOR]

The continuous state system is a special kind of a system in which the states of the system and its components have continuous values, ranging from perfect functioning to complete failure. This paper introduces the performance improvement for a continuous state system, which can be used to measure the improvement of systems performance comparing pre- and postmaintenance time. The probabilistic characteristics of performance improvement are discussed in detail. Then, the performance improvement for multicomponent maintenance and corresponding calculation method are also put forward to establish the objective function for maintenance optimization. Third, a maintenance optimization model for such a system is studied, and corresponding performance improvement based genetic algorithm is provided to search a near global optimal solution. Finally, two numerical examples and an oil transportation system application case study are implemented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Many technical systems are operated under the impact of external factors that may cause the systems to fail. For such systems, an interesting question is how those external factors and their impacts on the system can be identified at an earlier stage. Importance measures in reliability engineering are used to prioritise weak components (or states) of a system. Component failures and the impact of external factors in the real world may be statistically dependent as external factors may affect system performance. This paper proposes a new importance measure for analysing the impact of external factors on system performance. The measure can evaluate the degree of the impact of external factors on the system and can therefore help engineers to identify the factors with the strong impact on the system performance. A real-world case study is used to illustrate its applicability. [ABSTRACT FROM AUTHOR]

In this article, a warm standby n-unit system is studied. The system is operational as long as there is one unit normal. The unit online, which has a lifetime distribution governed by a phase-type distribution, is also attacked by a shock from some external causes. Assume that shocks arrive according to a Poisson process. Whenever an interarrival time of shock is less than a threshold, the unit online fails. The lifetimes of the units in warm standby is exponentially distributed. A repair man who can take multiple vacations repairs the failed units based on the "first-in-first-out" rule. The repair times and the vacation times of repairman are governed by different phase-type distributions. For this system, theMarkov process governing the system is constructed. The system is studied in a transient and stationary regime; the availability, the reliability, the rates of occurrence of the different types of failures, and the working probability of the repairman are calculated. A numerical application is performed to illustrate the calculations. [ABSTRACT FROM AUTHOR]

The performance of the system is often influenced by the performance of a particular component. Hence, it is important to identify the state changing of which component dominates the system performance changing in a maintenance process. Motivated by the Griffith importance measure (GIM) model, this paper proposed the generalized GIM, which extends the application of GIM to complex multi-state components, to evaluate the accurate contribution of the components in the changing of the system performance by considering the transition probabilities of the states of each component. Furthermore, the generalized GIM method is successfully applied in the continuous-state systems by extending the system structure function in GIM model. As a result, the expression of the performance of continuous system and the generalized GIM of the continuous-state components are proposed. A numerical example and an application to an oil transportation system are presented to illustrate how the proposed method works. [ABSTRACT FROM AUTHOR]

Importance measures in reliability engineering are used to identify weak components of a system and signify the roles of components in contributing to proper functioning of the system. Recently, an integrated importance measure (IIM) has been proposed to evaluate how the transition of component states affects the system performance based on the probability distributions and transition rates of component states. In the system operation phase, the bathtub curve presents the change of the transition rate of component states with time, which can be described by three different Weibull distributions. The behavior of a system under such distributions can be modeled by the semi-Markov process. So, based on the reported IIM equations of component states, this paper studies how the transition of component states affects system performance under the semi-Markov process. This measure can provide useful information for preventive actions (such as monitoring enhancement, construction improvement, etc.), and provide support to improve system performance. Finally, a simple numerical example is presented to illustrate the utilization of the proposed method. [ABSTRACT FROM AUTHOR]

Importance measures in reliability systems are used to identify weak components in contributing to proper functioning of the system. Traditional importance measures mainly concern the change of the system reliability as the change of the reliability of one component and seldom consider the expected number of repairs of the objective component in unit time. This paper proposes an improvement potential rate importance (IPR) to verify the effectiveness of the improvement in system reliability for multi-state repairablek-out-of-n:Gsystems. Then the comparisons between IPR and Birnbaum importance are discussed. Finally, a case study is given to demonstrate the proposed IPR. [ABSTRACT FROM AUTHOR]

Importance measures are widely used to characterize the roles of components in systems. The system lifetime can be divided into different life stages. Traditionally, importance measures do not consider the possible effect of the expected number of component failures over a system's lifetime and over different life stages, which, however, has a great effect on the system performance changes, and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) from unit time to system lifetime, and to different life stages. Based on the renewal functions of components, this measure can evaluate the changes of the system performance due to component failures. This generalization of the IIM describes which component is the most important to improve the performance of the system during the system lifetime and at different life stages. An example of the application of an oil transportation system is presented to illustrate the use of the generalized IIM. [ABSTRACT FROM PUBLISHER]

Abstract: Importance measures in reliability engineering are used to identify weak components and/or states in contributing to the reliable functioning of a system. Traditionally, importance measures do not consider the possible effect of groups of transition rates among different component states, which, however, has great effect on the component probability distribution and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) to estimate the effect of a component residing at certain states on the performance of the entire multi-state systems. This generalization of IIM describes in which state it is most worthy to keep the component to provide the desired level of system performance, and which component is the most important to keep in some state and above for improving the performance of the system. An application to an oil transportation system is presented to illustrate the use of the suggested importance measure. [Copyright &y& Elsevier]

Joint reliability importance (JRI) evaluates the interaction of two components in contributing to the system reliability in a system. Traditional JRI measures mainly concern the change of the system reliability caused by the interactive change of the reliabilities of the two components and seldom consider the probability distributions, transition rates of the object component states, and system performance. This article extends the JRI concept of two components from multi-state systems to multi-state transition systems and mainly focuses on the joint integrated importance measure (JIIM) which considers the transition rates of component states. Firstly, the concept and physical meaning of JIIM in binary systems are described. Secondly, the JIIM for deterioration process (JIIMDP) and the JIIM for maintenance process (JIIMMP) in multi-state systems are studied respectively. The corresponding characteristics of JIIMDP and JIIMMP for series and parallel systems are also analyzed. Finally, an application to an offshore electrical power generation system is given to demonstrate the proposed JIIM. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the integrated importance measure (IIM) of component states based on loss of system performance. To describe the impact of each component state, we first introduce the performance function of the multi-state system. Then, we present the definition of IIM of component states. We demonstrate its corresponding physical meaning, and then analyze the relationships between IIM and Griffith importance, Wu importance, and Natvig importance. Secondly, we present the evaluation method of IIM for multi-state systems. Thirdly, the characteristics of IIM of component states are discussed. Finally, we demonstrate a numerical example, and an application to an offshore oil and gas production system for IIM to verify the proposed method. The results show that 1) the IIM of component states concerns not only the probability distributions and transition intensities of the states of the object component, but also the change in the system performance under the change of the state distribution of the object component; and 2) IIM can be used to identify the key state of a component that affects the system performance most. [ABSTRACT FROM PUBLISHER]

• The machine degradation caused by the impact of low-quality feedstocks is modeled. • The mixture model is proposed to depict machine reliability with heterogeneous feedstocks. • The stochastic model is constructed to showcase the flow of low-quality products. • An effective computational framework is proposed to analyze system performance. Heterogeneity of feedstock quality can bring disturbances to machine degradation and product quality. Therefore, machine reliability modeling and machining quality analysis considering the flow of heterogeneous feedstocks are very essential in the performance evaluation of manufacturing systems. This paper presents a new mixture degradation model to evaluate the reliability of manufacturing machines that accounts for the flow and impact of heterogeneous feedstocks in serial-parallel manufacturing systems. Specifically, this mixture model leverages two Weibull distributions to describe machine degradation processes under the conditions of high-quality and low-quality feedstocks. Then, the flow of low-quality feedstocks is modeled by the non-homogeneous Poisson process, so that an interacting chain of quality and reliability is formulated for the serial-parallel manufacturing system. Based on the proposed model, an evaluation framework for the performance of serial-parallel manufacturing systems is provided. Finally, simulation experiments are implemented to analyze the operation status and quality loss of the system. The results showed the effectiveness of the proposed method in performance modeling of serial-parallel manufacturing systems with mixture machine degradation. The proposed approach shows strong potentials for general applications in the performance analysis of complex-structure manufacturing systems. [ABSTRACT FROM AUTHOR]