Integrated importance measure for multi-state coherent systems of k level

To verify the effectiveness of the integrated importance measure （IIM） for multi-state coherent systems of k level, the definition and physical meaning of IIM are demonstrated. Then, the improvement potential and Δ-importance measures are generalized to multi-state coherent systems based on the system performance level, and the relationships between IIM and traditional importance measures are discussed. The characteristics of IIM are demonstrated in both series and parallel systems. Also, an application to an oil transportation system is given. The comparison results show that： （i） IIM has some useful properties that are not possessed by traditional importance measures; （ii） IIM is effective in evaluating the component role in multi-state systems when the component reliability and the failure rate are simultaneously considered.

Variable Importance Measure System Based on Advanced Random Forest

The variable importance measure(VIM)can be implemented to rank or select important variables,which can effectively reduce the variable dimension and shorten the computational time.Random forest(RF)is an ensemble learning method by constructing multiple decision trees.In order to improve the prediction accuracy of random forest,advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees.Referring to the Mean Decrease Accuracy(MDA)index based on Out-of-Bag(OOB)data,the single variable,group variables and correlated variables importance measures are proposed to establish a complete VIM system on the basis of advanced random forest.The link of MDA and variance-based sensitivity total index is explored,and then the corresponding relationship of proposed VIM indices and variance-based global sensitivity indices are constructed,which gives a novel way to solve variance-based global sensitivity.Finally,several numerical and engineering examples are given to verify the effectiveness of proposed VIM system and the validity of the established relationship.

Dependency-based importance measures of components in mechatronic systems with complex network theory

To compensate for the limitations of previous studies,a complex network-based method is developed for determining importance measures,which combines the functional roles of the components of a mechatronic system and their topological positions.First,the dependencies among the components are well-represented and well-calculated.Second,a mechatronic system is modeled as a weighted and directional functional dependency network(FDN),in which the node weights are determined by the functional roles of components in the system and their topological positions in the complex network whereas the edge weights are represented by dependency strengths.Third,given that the PageRank algorithm cannot calculate the dependency strengths among components,an improved PageRank importance measure(IPIM)algorithm is proposed,which combines the node weights and edge weights of complex networks.IPIM also considers the importance of neighboring components.Finally,a case study is conducted to investigate the accuracy of the proposed method.Results show that the method can effectively determine the importance measures of components.

Importance Measure Analysis for Use in Dynamic Performance Design of a Co-cured Composite Damping Instrument Panel

For the best dynamic performance of a co-cured composite damping instrument panel with light weight and high strength, a multilayer sandwich structure with polymethaerylimide （PMI） foam combined with embedded and co-cured composite damping structure is proposed. The struetue can maintain the excellent mechanical properties of composite materials, and achieve the damping and light effect at the same time. Input variables which may affect the dynamic performance of the instrument panel were selected and variance based importance measure was analyzed through multi- finite element method （FEM） analysis. Using the results of the importance measure analysis, with other design requirements, the important design variable was optimized and an instrument panel with the best dynamic performance under the requirements of light weight and high strength was obtained. The structure of the instrument panel can provide reference for the design of precision, high speed, and dynamic composite component. The importance measure analysis of dynamic performance of the instrument panel can provide a reference for relative design.

Characteristics of Integrated Importance Measure for Whole Lifetime-Oriented System Performance Evaluation

Importance measures are being widely used to characterize the importance of component in systems.Focus on the integrated importance measure(IIM)of the whole lifetime measure based on the transition rates of component states.To describe the impact of each component in whole lifetime,the IIM is generalized in nonrepairable and repairable systems at first.Then,their formulas are computed in series and parallel systems.Finally,the characteristics of generalized IIM in typical series systems and parallel systems are analyzed.The results show that the generalized IIM of component can evaluate the expected effect of component state on the system performance in whole lifetime.

Importance measure based optimal maintenance policy for deteriorating components

An optimal maintenance policy tor deteriorating components based on quasi renew-process model is presented. In this policy, the first N - 1 failures of a component are maintained by repairs and the N＇h failure is maintained by replacement. The policy takes replacement actions at component lev- el by considering the fact that more and more components are designed to be field replaceable and maintenance activities are setting free from system halt. Concerning system structure impact, impor- tance measure is employed in the optimization procedure which aims at maximizing the long-rnn prof- it per unit time. Two example series parallel systems are taken to illustrate the policy and it is proved to work well. According to importance analysis, components are classified into important ones and unimportant ones based on the system behavior under their failures. Simulation results show that the presented policy makes a clear distinction between them and takes effective mainte- nance actions to compensate the deteriorating of components.

Importance measure-based maintenance strategy optimization:Fundamentals,applications and future directions in AI and IoT

Numerous maintenance strategies have been proposed in the literature related to reliability.This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies.We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research.The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies.Additionally,it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies.Finally,it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

Importance measure system of fuzzy and random input variables and its solution by point estimates

For structure system with fuzzy input variables as well as random ones, a new importance measure system is presented for evaluating the effects of the two kinds of input variables on the output response. Based on the fact that the fuzziness of the output response is determined by that of the input variable, the presented measure system defines the importance measures which evaluate the effect of the fuzzy input variable. And for the random input variable, the importance measure system analyzes its effect from two aspects, i.e. its effect on the central distribution position and that on the fuzzy degree of the membership function of the output response. Taking the effects of the two kinds of input variables on the first moment and second one of the output response into account, the definitions of the importance measures of the input variables are given and their engineering significations are demonstrated. Combining with the advantages of the point estimates of Zhao and Ono, a solution of the proposed importance measures is provided. Several examples show that the proposed measure system is comprehensive and reasonable, and the proposed solution can improve computational efficiency considerably with acceptable precision.

Recent advances in system reliability optimization driven by importance measures

System reliability optimization problems have been widely discussed to maximize system reliability with resource constraints.Bimbaum importance is a wellknown method for evaluating the effect of component reliability on system reliability.Many importance measures(IMs)are extended for binary,multistate,and continuous systems from different aspects based on the Bimbaum importance.Recently,these IMs have been applied in allocating limited resources to the component to maximize system performance.Therefore,the significance of Bimbaum importance is illustrated from the perspective of probability principle and gradient geometrical sense.Furthermore,the equations of various extended IMs are provided subsequently.The rules for simple optimization problems are summarized to enhance system reliability by using ranking or heuristic methods based on IMs.The importance-based optimization algorithms for complex or large-scale systems are generalized to obtain remarkable solutions by using IM-based local search or simplification methods.Furthermore,a general framework driven by IM is developed to solve optimization problems.Finally,some challenges in system reliability optimization that need to be solved in the future are presented.

Possibilistic entropy-based measure of importance in fault tree analysis

With respect to the subjective factors and nonlinear characteristics inherent in the important identification of fault tree analysis （FTA）, a new important measure of FTA is proposed based on possibilistic information entropy. After investigating possibilistic information semantics, measure-theoretic terms, and entropy-like models, a two-dimensional framework has been constructed by combining both the set theory and the measure theory. By adopting the possibilistic assumption in place of the probabilistic one, an axiomatic index of importance is defined in the possibility space and then the modelling principles are presented. An example of the fault tree is thus provided, along with the concordance analysis and other discussions. The more conservative numerical results of importance rankings, which involve the more choices can be viewed as “soft” fault identification under a certain expected value. In the end, extension to evidence space and further research perspectives are discussed.

Transformer Fault Analysis Based on Bayesian Networks and Importance Measures

Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network(BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method.

Analysis of Phased-Mission System Reliability and Importance with Imperfect Coverage

Accounting for static phased-mission systems (PMS) and imperfect coverage (IPC), generalized and integrated algorithm (GPMS-CPR) implemented a synthesis of several approaches into a single methodology whose advantages were in the low computational complexity, broad applicability, and easy implementation. The approach is extended into analysis of each phase in the whole mission. Based on Fussell-Vesely importance measure, a simple and efficient importance measure is presented to analyze component’s importance of phased-mission systems considering imperfect coverage.

Important Economic Reform Measures in 1994

1994 was of special significance to the reform of China’s economic system. The new reform measures for taxation, finance, foreign trade, investment, and the price and enterprise system were smoothly implemented in the past year. In the reform of the taxation system, the taxation quota assigned by central government for the enterprises in the provinces and municipalities, regardless of their actual profits and losses, was replaced by a system in which tax was levied in proportion to the business turnover and profit. A turnover tax system with added-value tax as its core

Measures for Administration of the Import of Mechanical and Electronic Products

The Measures for the Administration of the Import of Mechanical and Electronic' Products co-formulated by the Ministry of Commerce,the General Administration of Customs and the General Administration of Quality Supervision,Inspection and Quarantine,was hereby promul- gated,which entered into force as of May 1,2008.

Maintenance Strategy of Multi-Component System Due to Degradation and Shock

The proposed maintenance strategy uses component grades and survival signature to describe and update system structure. A system dynamic stress-strength reliability(SSR) model was established to describe component failure processes because of internal degradation and external shock. Besides, a corrective maintenance rule and two-level preventive maintenance rule made up the proposed maintenance strategy. The former combined sequentially minimal repair and corrective replacement(SMRCR) based on system structure updating. The latter considered group importance measure based on the system dynamic SSR and preventive maintenance cost. Further, a cost model was developed by the proposed strategy, and the optimal decision variables were found by genetic algorithm. Finally, using a case system to illustrate the above strategy improved system and reduced maintenance costs.

Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network

In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately.

Feature Selection for Intrusion Detection Using Random Forest

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.

Terrain Simplification Research in Augmented Scene Modeling

Augmented reality is the merging of synthetic sensory information into a user's perception of a real environment. As one of the most important tasks in augmented scene modeling, terrain simplification research has gained more and more attention. In this paper, we mainly focus on point selection problem in terrain simplification using triangulated irregular network. Based on the analysis and comparison of traditional importance measures for each input point, we put forward a new importance measure based on local entropy. The results demonstrate that the local entropy criterion has a better performance than any traditional methods. In addition, it can effectively conquer the 'short-sight' problem associated with the traditional methods.

Reliability Optimization in the Islanded Mode of Microgrid

A microgrid is a combination of distributed energy resources and controllable loads. The main objective of this research is to optimize energy flow within a microgrid with regards to reliability in grid connected mode. A microgrid with combined heat and power, natural gas generator, diesel generator, solar energy, wind energy, and battery energy storage along with a critical load is considered in this research. An event oriented analytical method called FTA （fault trees analysis） is implemented for reliability optimization using PTC Windchill Solutions software in a microgrid. The reliability of each component in each energy source of the microgrid is calculated using FTA. The reliability of the critical load is evaluated. The quantitative and qualitative results of FTA are evaluated in order to interpret the results of fault tree. The sensitivity and uncertainty of the fault tree results for critical load is deduced by calculating the importance measures such as risk achievement worth, risk reduction worth, criticality importance and Fussel-Vesely importance. Finally from the results the components that are sensitive and at high risk are deduced.

Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm

In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.