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The development of society has put forward higher requirements for the supply of maintenance spare parts. However, the current support index is not comprehensive enough to evaluate the system ability. Meanwhile, the influence of horizontal supply time on spare parts storage is not fully reflected. Therefore, the problem of spare parts storage configuration in two-echelon maintenance and supply support systems is analysed in the paper. First, the state transition rates of different parts are studied under the context. Then a system-level comprehensive support rate based on importance measure is proposed. To determine the number of different spare parts, an optimisation model is established to maximise system support efficiency. Furthermore, a site importance measure based on horizontal supply time is proposed to measure the site priority over spare parts storage. Next, a two-stage decision method is presented for determining the location of spare parts. Finally, the feasibility of the proposed method is verified with the help of an example for the aircraft maintenance and supply support system. [ABSTRACT FROM AUTHOR]

In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage. [ABSTRACT FROM AUTHOR]

In this paper, a practical maintenance algorithm is proposed to improve the reliability of actuation systems and their components, specifically addressing the consistency degradation caused by faults in the symmetric actuation system components of more electric aircraft (MEA). By integrating important measures with traditional genetic algorithms, the accuracy of the algorithm is improved. Prior to maintenance, a reasonable classification of components is built to mitigate the adverse effects of extreme fault conditions on the algorithm. This approach improves both the effectiveness and efficiency of the algorithm, rendering the overall maintenance strategy better suited for real-world needs. Finally, comparative simulations confirm the algorithm's superior performance in reliability improvement, demonstrating its substantial contribution to the field of MEA maintenance and reliability. [ABSTRACT FROM AUTHOR]

With the development of economic globalization and increasing international trade, the maritime transportation system (MTS) is becoming more and more complex. A failure of any supply line in the MTS can seriously affect the operation of the system. Resilience describes the ability of a system to withstand or recover from a disaster and is therefore an important method of disaster management in MTS. This paper analyzes the impact of disasters on MTS, using the data of Suez Canal "Century of Congestion" as an example. In practice, the severity of a disaster is dynamic. This paper categorizes disasters into different levels, which are then modelled by the Markov chain. The concept of a repair line set is proposed and is determined with the aim to minimize the total loss and maximize the resilience increment of the line to the system. The resilience measure of MTS is defined to determine the repair line sequence in the repair line set. Finally, a maritime transportation system network from the Far East to the Mediterranean Sea is used to validate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a practical maintenance algorithm is proposed to improve the reliability of actuation systems and their components, specifically addressing the consistency degradation caused by faults in the symmetric actuation system components of more electric aircraft (MEA). By integrating important measures with traditional genetic algorithms, the accuracy of the algorithm is improved. Prior to maintenance, a reasonable classification of components is built to mitigate the adverse effects of extreme fault conditions on the algorithm. This approach improves both the effectiveness and efficiency of the algorithm, rendering the overall maintenance strategy better suited for real-world needs. Finally, comparative simulations confirm the algorithm’s superior performance in reliability improvement, demonstrating its substantial contribution to the field of MEA maintenance and reliability.

This paper introduces innovative sensitivity indices based on Cliff’s Delta for the global sensitivity analysis of structural reliability. These indices build on the Sobol’ method, using binary outcomes (success or failure), but avoid the need to calculate failure probability Pf and the associated distributional assumptions of resistance R and load F. Cliff’s Delta, originally used for ordinal data, evaluates the dominance of resistance over load without specific assumptions. The mathematical formulations for computing Cliff’s Delta between R and F quantify structural reliability by assessing the random realizations of R > F using a double-nested-loop approach. The derived sensitivity indices, based on the squared value of Cliff’s Delta δC2, exhibit properties analogous to those in the Sobol’ sensitivity analysis, including first-order, second-order, and higher-order indices. This provides a framework for evaluating the contributions of input variables on structural reliability. The results demonstrate that the Cliff’s Delta method provides a more accurate estimate of Pf. In one case study, the Cliff’s Delta approach reduces the standard deviation of Pf estimates across various Monte Carlo run counts. This method is particularly significant for FEM applications, where repeated simulations of R or F are computationally intensive. The double-nested-loop algorithm of Cliff’s Delta maximizes the extraction of information about structural reliability from these simulations. However, the high computational demand of Cliff’s Delta is a disadvantage. Future research should optimize computational demands, especially for small values of Pf.

The lack of effective failure correlation analysis is one main reason for the gap between the reliability models and the actual complex systems with mixed static and dynamic characteristics. Takagi and Sugeno (T-S) dynamic fault tree is one powerful tool to analyze the static and dynamic failure logic relationship but it assumes the failure probability of the event is independent. Therefore, this paper proposes a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method integrates the failure probability distribution function of basic events with multi-factors and the multi-dimensional copula function, and the important measure of this method is also deduced. The reliability model expression for systems with failure correlations, both in series and in parallel, is discussed and verified. Compare the proposed method with the assumption that the probability of a failure event is independent. This method solves the problem of a large error when ignoring the failure correlation between parts and the degree of the correlation between variables can be characterized. The reliability analysis can be conducted on complex systems affected both by multi-factors and failure correlations. The proposed method is applied to the reliability analysis of a hydraulic height adjustment system and the correctness and superiority of the method are verified. [ABSTRACT FROM AUTHOR]

The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability. [ABSTRACT FROM AUTHOR]

The phased-mission reliability of unmanned aerial vehicle (UAV) swarm refers to its capability to successfully complete the missions of each phase under specified conditions for a specified period. In order to study the reliability of phased-mission in UAV swarm, this paper firstly studies the reliability of a single UAV under fault coverage. Then, considering the mission characteristics of UAV swarm, the consecutive k-out-of-n system is studied to model and predict the reliability of UAV swarm phase mission. Some importance measures are introduced to analyze the influence of UAV in different positions on the reliability of the whole system. Finally, numerical examples of linear and circular UAV swarms are given to demonstrate and verify the correctness of the model. The reliability modeling established in this paper can predict the phased-mission reliability of UAV swarm scientifically.

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

In the reliability theory, the performance of a system can be improved by different methods, such as redundancy and reduction methods. The redundancy method may not be optimal when some restrictions such as volume and weight are crucial. In the reduction method, system reliability is increased by reducing the failure rate of some of its components by a factor 0 < ρ < 1 which is called the reliability equivalence factor (REF). This article considers a new light on reliability equivalence factors in a coherent system with independent components. A closed form for ρ is obtained when the reduction method is applied on a single component of the system. Based on this, we also define a new measure of component importance. Various numerical illustrative examples are given to support the new results. [ABSTRACT FROM AUTHOR]

Importance and joint importance measures in reliability engineering are used to identify the weak areas of a system and signify the roles of components in either causing or contributing to proper functioning of the system. This paper introduces, for two multistate components, Joint Birnbaum-Reliability Achievement Worth (JBRAW), Joint Birnbaum-Reliability Reduction Worth (JBRRW), Joint Birnbaum-Reliability Fussell-Vesely measure (JBRFV) and analogues joint risk importance measures for the multistate system. A steady state performance level distribution with restriction to the component's states is used to obtain the introduced measures. Universal generating function (UGF) technique is applied for the evaluation of proposed joint importance measures. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans. [ABSTRACT FROM AUTHOR]

Maintenance can improve a system’s reliability in a long operation period or when a component has failed. The reliability modeling method that uses the stochastic process degradation model to describe the system degradation process has been widely used. However, the existing reliability models established using stochastic processes only consider the internal degradation process, and do not fully consider the impact of external random shocks on their reliability modeling. Furthermore, the existing theory of importance does not consider the actual factors of maintenance cost. In this paper, based on the reliability modeling of random processes, the degradation rate under the influence of random shocks is introduced into the time scale function to solve the impact of random shocks on product reliability, and two cost importance measures are proposed to guide the maintenance selection of the components under limited resources in the system. Finally, a subsystem of an aircraft hydraulic system is analyzed to verify the proposed method’s performance. [ABSTRACT FROM AUTHOR]

Many production systems are multistate, with a finite number of performance levels that are between perfect functioning and complete failure. Importance measures are often used in the maintenance planning of complicated systems, to observe the criticalities of components, reveal the system weakness, and thus to guide the allocation of limited maintenance resources. This paper compares several commonly used importance measures for multistate systems, and investigates their effectiveness and limitations with a simple example. These existing measures focus on the states of a system at some moment, while ignoring the dynamic behaviours in the long-term. For a production system, however, its throughput in a certain period, rather than the instantaneous performance, is the system property of interest. Therefore, two new long-term throughput-based importance measures: total throughput importance measure and maintenance effect importance measure are proposed in this paper, to answer the questions about the criticalities of different components and the long-term effects of successful maintenance activities on the throughput of a production system in a certain period. A case study on an offshore production system is conducted, to illustrate how the new importance measures work and what kind of implications can be provided to the maintenance crew. [ABSTRACT FROM AUTHOR]

This paper introduces an efficient methodology for evaluating the first four central moments of performance functions in structural reliability assessment. The procedure of the proposed method is divided into three major steps. First, a performance function is approximated as the sum of univariate and bivariate functions by the bivariate dimension-reduction method. Next, a new criterion based on importance measure analysis is proposed for dividing the bivariate functions into significant bivariate functions and non-significant bivariate functions, and then the new approximation of the performance function is obtained. Finally, formulas are derived for evaluating the first four central moments of the proposed performance function, in which the first four raw moments of the univariate and bivariate functions can be achieved by a point estimating method in a standard normal space. The effectiveness and efficiency of the proposed method are demonstrated through three numerical examples.

Hepatocellular carcinoma (HCC) is a common malignant tumor. The prognostic of patients after operation involves many factors contributing to survival, and the long-term survival is unsatisfactory. Importance measures can be used to deal with other virtual systems to identify the critical factors influencing a result. In this paper, importance measures are used to analyze the prognostic factors influencing the survival time of HCC patients. Survival time and influencing factors are treated as systems and components to build a prognostic Bayesian network (BN) model based on a real-world data set. The importance values of factors are calculated using a BN-based importance calculation method. The sequence of factors based on importance measures is compared with the sequence obtained by Cox regression model to prove the effectiveness of importance measures in dealing with the prognostic influencing factors analysis of HCC patients. [ABSTRACT FROM AUTHOR]

A predefined production level should be achieved to meet the market demand and customer satisfaction. Therefore, in cases that the system cannot reach the desired production level, throughput capacity should be improved. Improving system throughput capacity can be obtained by improvement in component performance. Since various components have different effects on system throughput capacity, thus, a component that has the greatest effect on system throughput capacity should be identified. This paper aims to investigate the application of analytical methods for throughput capacity analysis of a system and suggest the throughput capacity importance measure, adapted from the Birnbaum importance measure. The throughput capacity importance measure quantifies the components' effect on system throughput capacity. The analysis result will be useful to identify the critical component/s from a system throughput capacity point of view. A case study demonstrates how it can be applied in a real case. [ABSTRACT FROM AUTHOR]

Availability is a comprehensive reflection of the overall performance for a manufacturing system, so it is necessary to evaluate the impact of component changes on the availability of a manufacturing system. However, most of the existing importance measures are based on reliability change or state degradation to measure the relative importance of components, which cannot be comprehensively analyzed for multi‐state manufacturing systems. The availability of a multi‐state manufacturing system is influenced by time and state. Therefore, considering the synergistic effect of time and state, this paper proposes an Integrated Availability Importance Measure (IAIM) based on proportional hazards model combining with component state probability and state transition rate. This measure fully examines the time parameters, state parameters and maintenance impact of multi‐state systems, and can comprehensively evaluate the relative importance of multi‐state coherent system components, provide theoretical basis for further developing the availability maintenance strategy, and indicate the improvement direction for further improving the reliability of multi‐state manufacturing systems. A numerical example is given to prove its feasibility, and the influence of related parameters is investigated by sensitivity analysis. Finally, a real‐world case study is used to illustrate its applicability. [ABSTRACT FROM AUTHOR]

Risk-informed safety classification of structures, systems and components (SSCs) is very important for ensuring the safety and economic efficiency of nuclear power plants (NPPs). However, previous methods for safety classification of SSCs do not take the plant operating modes or the operational process of SSCs into consideration, thus cannot concentrate on the safety and economic efficiency accurately. In this contribution, a new method for safety classification of SSCs based on the categorization of plant operating modes is proposed, which considers the NPPs operating history to improve the economic efficiencies while maintaining the safety. According to the time duration of plant configurations in plant operating modes, average importances of SSCs are accessed for an NPP considering the operational process, and then safety classification of SSCs is performed for plant operating modes. The correctness and effectiveness of the proposed method is demonstrated by application in an NPP's safety classification of SSCs.

The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability.

The metro network plays a vital role in the urban transportation system. However, the metro network is easily damaged by humans and natural disturbances. This can cause serious economic damage, such as the suspension of metro station operations and line disruptions. Therefore, we conducted this study in order to minimize the loss caused by the damage to the metro network and improve the performance of the network after recovery. Based on the cascading failures of metro networks, this paper proposes a recovery model for metro networks considering the value of time. Then, considering the time value, a new node importance measure is proposed using the determination of maintenance priorities. The maintenance priorities of nodes with different importance values are investigated to minimize network losses. Lastly, the applicability of the method is verified by a metro network in Zhengzhou city. [ABSTRACT FROM AUTHOR]

This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. The measure of reliability is failure probability. The failure probability is analysed using the Bernoulli distribution with binary outcomes of success (0) and failure (1). Deeper connections between Shannon entropy and variance are explored. Model uncertainties increase the heterogeneity in the data 0 and 1. The article proposes a new methodology for quantifying model uncertainties based on the equality of variance and entropy. This methodology is briefly called "variance = entropy". It is useful for stochastic computational models without additional information. The "variance = entropy" rule estimates the "safe" failure probability with the added effect of model uncertainties without adding random variables to the computational model. Case studies are presented with seven variants of model uncertainties that can increase the variance to the entropy value. Although model uncertainties are justified in the assessment of reliability, they can distort the results of the global sensitivity analysis of the basic input variables. The solution to this problem is a global sensitivity analysis of failure probability without added model uncertainties. This paper shows that Shannon entropy is a good sensitivity measure that is useful for quantifying model uncertainties. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Filter pruning is one of the most effective approaches to reduce the storage and computational cost of convolutional neural networks. How to measure the importance of each filter is the key problem for filter pruning. In this work, we propose a novel method that can evaluate the importance of each filter and gradually prunes those filters with small scores. Specifically, the importance is obtained via probing the effect of each filter on the task-related loss function by randomly pruning the original network. The smaller the effect on the task-related loss function, the lower the importance of the filter. It's worth noting that our method is scale consistent across all layers without requiring layer-wise sensitivity analysis, which can be used to prune various networks, including ResNet and DenseNet. Extensive experiments demonstrate the outstanding performance of our method. For example, on ILSVRC-2012, our method can prune 42.74% floating point operations and 39.61% parameters of ResNet-50 with only 0.73% Top-1 accuracy loss and 0.37% Top-5 accuracy loss. [ABSTRACT FROM AUTHOR]

Pipe system conveying fluid faces the problem of multi-order resonance failure caused by broadband excitation. For solving above problem, the dynamic stiffness method is employed to solve the dynamic equations of multi-span pipes considering the temperature effect. Combining the obtained natural frequency and the rule of resonance failure of pipe system, a multi-order anti-resonance system reliability model is established in this paper. To analyze the effect of input variable uncertainty on the probability of system resonance failure, the variance-based importance measurement index is further established. By introducing the active learning Kriging (ALK) model, the resonance failure probability and importance measurement index can be calculated efficiently. The effects of fluid flow velocity, pressure and temperature on the probability of pipe resonance failure are analyzed in detail, which has significant guidance for the anti-resonance optimization design of pipes.

This article formulates risk-based component importance measures (RCIMs) to identify critical components for a bridge subjected to earthquakes. RCIM, unlike traditional reliability-based importance measures, is well suited for complex systems like bridges as it uses a flexible system failure definition using bridge level risk consequences or performance objectives. Contrasting traditional notions of risk and reliability related to structural system failures, the RCIMs embrace a broader definition of risk which includes consequences of system failure to society and the environment. System failure is defined as exceedance of a user-defined threshold of system risk (e.g. based on cost, emissions, embodied energy, or other metrics). Our definition of system failure using global performance metrics helps relating component to system reliability explicitly and offers an alternative perspective to aggregate joint failure events of bridge components into relevant damage states. RCIM combines information about the reliability of a component and its contribution to the system performance and hence the importance measures developed offer risk mitigation indicators for decision makers as to which components may require upgrade to achieve a given performance objective. The proposed RCIMs generalize existing importance measures through analytical exploration of the entire space of system configurations with correlated component failures, while also considering multiple risk-based criteria beyond reliability. These RCIMs, demonstrated through a seismic bridge system case study, further show that as the bridge components age and the hazard intensity varies, the relative contribution of the components to system risk also shifts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Develop the digital twin-enhanced opportunistic maintenance of microgrid systems. • Measure the consequences of equipment failure with CM costs, PM costs. • Analyze the maintenance sequence and number of devices under limited resources. • Give a risk model of the microgrid system after equipment failure. • Introduce a risk importance measure to guide opportunistic maintenance. Smart microgrids face more diverse and frequent risks than traditional grids due to their complexity and reliance on distributed generation. Ensuring the reliable operation of smart microgrids requires effective maintenance. Traditional maintenance methods, based on periodic inspections and fault diagnosis, struggle to adapt to the dynamics and complexity of microgrid systems. The introduction of digital twin technology provides a new solution for the opportunistic maintenance of microgrid systems. This paper presents a digital twin microgrid architecture for real-time monitoring and decision-making in opportunistic maintenance. Meanwhile, this paper introduces a risk importance measure to aid to optimize opportunistic maintenance strategies when resources are limited. Finally, a wind-solar-storage microgrid is used to illustrate the proposed method. Experimental results show that the proposed method significantly reduces maintenance costs and improves system reliability, effectively supporting microgrid maintenance. [ABSTRACT FROM AUTHOR]

• Dependence among components and dependence between suffered failure processes within each component are simultaneously modeled. • System reliability is comprehensively analyzed under hierarchical dependence. • Novel importance measures are proposed to identify the key component and its critical failure process. This paper explicitly modeled the hierarchical dependences existing in multi-state systems (MSSs) and proposed novel importance measures (IMs) to rank the criticality of failure-induced factors in different levels. Dependence existing within the component is specified as that arrived shocks make the components become more susceptible to the inherent multi-state degradation. The interaction among components refers to the fact that the degradation of a particular component can be accelerated by others. A comprehensive framework based on Semi-Markov process is constructed to jointly analyze the dependences above. By using the proposed IMs, contributions of each comprising component are firstly measured under hierarchical dependences. Secondly, influences brought by the triggering components on the influenced component are ranked so that the dependence strength among components can be measured. Then, criticality analysis for the suffered failure processes is conducted to specify how the failure processes affect the component reliability level. At last, an illustrative example about a wind turbine generator is presented to show how the proposed IMs can be applied for identifying the key component and its critical failure process. [ABSTRACT FROM AUTHOR]

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

In the structural system with multiple components under fuzzy inputs, the influence of each component on system failure needs to be considered, because it is of great significance to simplify the system model as well as improve the system’s performance. In this paper, the criticality fuzzy safety importance measure of the multicomponent system under fuzzy inputs is first defined to measure the contribution of each component to the system failure. The defined criticality fuzzy safety importance measure can be applied to fault diagnosis, i.e., when a system fails, the component with the largest importance is the most probable component causing failure, thus should be checked first. Then, a method combining the multivariate Gaussian process and fuzzy simulation is proposed to estimate the criticality fuzzy safety importance measure and rank the importance of components by taking the correlation of components into consideration. Finally, several examples are employed to analyze the component importance as well as to illustrate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In order to evaluate the mission success of system of systems (SoS) and to develop maintenance strategies to meet mission requirements of SoS, an importance measure method of equipment task based on operational dependency of SoS is proposed. The operational dependency network of SoS is constructed based on FDNA, and the nodes and dependency relationships in the network are determined. According to the Markov process, the reliability model of a system function is constructed, and the operability level and probability of each node in the network are obtained. Meanwhile, the algorithm of Birnbaum importance measure is introduced to effectively quantify the impact of operational dependency on the task importance of each equipment system in SoS. Finally, the feasibility and effectiveness of this method is verified by a case study.

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not the possibility to treat or use this type of gas. Concerns about global warming led to several initiatives for reducing flaring or even eliminating combustion. Treating flare gas was made possible by the introduction of flare gas recovery systems that have become increasingly obligatory. Most solutions add a flare gas recovery system to an existing flare system. In a typical scenario, after analyzing the existing facility and collecting the necessary data, alternative designs are proposed and criteria are determined to make a choice between the proposed alternatives. In this paper two designs of a gas control system are proposed, and reliability was chosen as the deciding factor. Using repairable dynamic fault trees, the failure models of the two designs have been implemented. Afterwards, a novel hybrid technique, the Stochastic Hybrid Fault Tree Automaton, is used to model the working conditions in which the system operates, with the aim to achieve a more realistic assessment and evaluate the disaster likelihood associated to these failures. It is shown that the latter enables a richer analysis where the effects of failure can be better assessed. This is important for correct choice between design alternatives because, as shown in the case study, the results of the two analyses can lead to contrasting conclusions of the solution to adopt. Further investigations have been carried out focusing on the safety sub-systems and on the basic events in each design. The Importance Measure analysis revealed that some of the components were responsible for most of the critical failures, thus locating some areas of possible design improvement.

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM. [ABSTRACT FROM AUTHOR]

The fluctuation of product quality is closely related to the degradation of the equipment in multi-stage manufacturing systems. This paper proposes a critical measure approach for a quality-reliability coupled network of multi-stage manufacturing systems via network controllability. The impact of component degradation will be transmitted, expanded, and accumulated in multiple manufacturing stages, leading to quality flaws or even shutdowns of the entire system. An important measurement method via controllability analysis is provided by quantifying the impact of attacking the quality-reliability coupled network. By quantifying the control ability of the fault source node on the key quality attribute node, the weakness that affects the processing accuracy of a production line is identified. Case studies of real production lines are applied to verify the effectiveness, and comparative results show the method can guide the quality-reliability improvement of manufacturing systems.

Failure of in-service oil and gas steel pipelines can have disastrous environmental, social and economic impacts. Preventing or alleviating those negative impacts requires a methodology to predict the service life of corroded steel pipes that enables implementing preventive maintenance and repairs measures. Such predictions involve uncertainty and vary in time. This paper puts forward a time-variant reliability-based approach to assess the failure of corroded offshore pipelines. The proposed approach accounts for the uncertainty in the corrosion rate and mechanical characteristics of pipes. Two methods are proposed: One that employs the Monte Carlo sampling and one that is based on the first-order reliability method. The methodology is then applied to three corroded offshore gas pipelines located in India and the timevariant failure probability is estimated considering target reliability. Next, variability and sensitivity analyses are conducted to identify the most influential sources of uncertainty on the failure probability of pipelines. It can be asserted that corrosion rate has a substantial role in estimating the structural reliability of pipelines. [ABSTRACT FROM AUTHOR]

The metro network plays a vital role in the urban transportation system. However, the metro network is easily damaged by humans and natural disturbances. This can cause serious economic damage, such as the suspension of metro station operations and line disruptions. Therefore, we conducted this study in order to minimize the loss caused by the damage to the metro network and improve the performance of the network after recovery. Based on the cascading failures of metro networks, this paper proposes a recovery model for metro networks considering the value of time. Then, considering the time value, a new node importance measure is proposed using the determination of maintenance priorities. The maintenance priorities of nodes with different importance values are investigated to minimize network losses. Lastly, the applicability of the method is verified by a metro network in Zhengzhou city.

In order to improve the reliability and performance of landing gear retraction systems, this paper presents two importance analysis methods based on universal grey operation. According to the system principle and fault mechanisms, the fault tree of the retraction system was first established. The uncertainties of the bottom events were then described using the universal grey number to obtain the universal grey representation of the system failure probability. And compared with the traditional interval operation, the results show that universal grey operation can solve the problem of interval expansion with uncertainty. Importance analyses of the bottom events were then conducted based on the probability importance and the key importance. By comparing the two important indices of the bottom events, the larger the value is, the higher the importance is. It was found that the occurrence of the bottom events "pipeline oil leakage," "pump motor damage," and "oil pollution" had the greatest impact on system failure probability, thus determining the key weak links affecting system failure and indicating the most effective targets for improvement. [ABSTRACT FROM AUTHOR]

In the marine industry, heave compensation systems are applied to marine equipment to compensate for the adverse effects of waves and the hydraulic system is usually used as the power system of heave compensation systems. This article introduces importance theory to the opportunistic maintenance (OM) strategy to provide guidance for the maintenance of heave compensation systems. The working principle of a semi-active heave compensation system and the specific working states of its hydraulic components are also first explained. Opportunistic maintenance is applied to the semi-active heave compensation system. Moreover, the joint integrated importance measure (JIIM) between different components at different moments is analyzed and used as the basis for the selection of components on which to perform PM, with the ultimate goal of delaying the degradation of the expected performance of the system. Finally, compared with conditional marginal reliability importance (CMRI)- based OM, the effectiveness of JIIM-based OM is verified by the Monte Carlo method. [ABSTRACT FROM AUTHOR]