Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Reliability Evaluation and Sensitivity Analysis for Multi-state System Based on Time Degradation Measures

The performance and state of multi-state system depend on its structure and different state combinations of the components. In order to evaluate the reliability of multi-state system effectively,a reliability evaluation and sensitivity analysis method based on the time degradation measures was proposed. The equivalence sets of the multi-state system under different output performances were established. The state combinations were classified according to the performance level. The degradation probability models under different states were established,and the new reliability measures,such as dynamic probability of multi-state system,holding time in each state,dynamic expectation function and integrated expectation function of the performance,were proposed and used to implement the dynamic reliability evaluation and sensitivity analysis. A certain diesel engine fuel feeding system was taken as an application example to illustrate the proposed method. The results show that not only the holding time in the desired state of the components and the system can be predicted,but also the best state component in a certain time period can be obtained.

Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults

In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools.

Development of diabetic complications and influencing factors among 32653 type 2 diabetes patients:retrospective cohort study using a multi-state Markov

Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospective cohort study was performed from 2009 to 2021.Type 2 diabetes patients who were first diagnosed after the age of 35 years between January 1,2009,and December 31,2017,were included.Five states were defined according to the number of chronic complications:no(S0),one(S1),two(S2),three(S3),and four or more complications(S4).A multi-state Markov model was constructed to estimate transition probability,transition intensity,mean sojourn time,and the possible factors for each state.Results:The study included 32653 type 2 diabetes patients(mean age,59.59 years;15929(48.8%)male),and mean follow-up time of 7.75 years.In all,4375 transitions were observed.The 12-year transition probability of from state S0 to S1 was the lowest at 16.4%,while that from S2 to S3 was the highest,at 45.6%.Higher fasting blood glucose,lower high-density lipoprotein cholesterol,higher total cholesterol,and an unhealthy diet were associated with higher risk of progression from S0 to S1.Being female,less than 60 years old,weekly physical activity,and vegetarian diet decreased this risk.Being female and less than 60 years old reduced the likelihood of transition from S1 to S2,whereas lower high-density lipoprotein cholesterol increased this likelihood.Conclusions:Following the occurrence of two complications in type 2 diabetes patients,the risk for accumulating a third complication within a short time is significantly increased.It is important to take advantage of the stable window period when patients have fewer than two complications,strengthen the monitoring of blood glucose and blood lipids,and encourage patients to maintain good living habits to prevent further deterioration.

A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data:An Application to COVID-19

Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.

Reliability Improvement of Large Multi-state Series-parallel Systems

In the paper, excess methods for improving the reliability of multi-state series-parallel systems are presented： for the hot reserve of single components, the cold reserve of single components, and the mixed （hot and cold） reserve of single components. A process is also introduced to improve the reliability of these methods by replacing their components with more reliable ones. New theorems for multi-state limit reliability functions in homogeneous and non-homogeneous series-parallel large systems composed of components with improved reliability are presented, and applied to compare the effects of these systems in different reliability improving methods.

Importance measure of system reliability upgrade for multi-state consecutive k-out-of-n systems

Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system. Traditional importance measures mainly concerned the changing value of the system reliability caused by the change of the reliability of the component, and seldom considered the joint effect of the probability distribution, improvement rate of the object component. This paper studies the rate of the system reliability upgrading with an improvement of the component reliability for the multi-state consecutive k-out-of-n system. To verify the multi-state consecutive k-out-of-n system reliability upgrading by improving one component based on its improvement rate, an increasing potential importance （IPI） and its physical meaning are described at first. Secondly, the relationship between the IPI and Birnbaum importance measures are discussed. And the IPI for some different improvement actions of the component is further discussed. Thirdly, the characteristics of the IPI are analyzed. Finally, an application to an oil pipeline system is given.

Capacitated stochastic coloured Petri net-based approach for computing two-terminal reliability of multi-state network

Classical network reliability problems assume both net- works and components have only binary states, fully working or fully failed states. But many actual networks are multi-state, such as communication networks and transportation networks. The nodes and arcs in the networks may be in intermediate states which are not fully working either fully failed. A simulation ap- proach for computing the two-terminal reliability of a multi-state network is described. Two-terminal reliability is defined as the probability that d units of demand can be supplied from the source to sink nodes under the time threshold T. The capacities of arcs may be in a stochastic state following any discrete or continuous distribution. The transmission time of each arc is also not a fixed number but stochastic according to its current capacity and de- mand. To solve this problem, a capacitated stochastic coloured Petri net is proposed for modelling the system behaviour. Places and transitions respectively stand for the nodes and arcs of a net- work. Capacitated transition and self-modified token colour with route information are defined to describe the multi-state network. By the simulation, the two-terminal reliability and node importance can be estimated and the optimal route whose reliability is highest can also be given. Finally, two examples of different kinds of multi- state networks are given.

A voltage support control strategy based on three-port flexible multi-state switch in distribution networks

Voltage sags in power system may lead to serious problems such as the off-grid of distributed generation and electrical equipment failures.As a novel type of power electronic equipment,a flexible multi-state switch(FMSS)is capable to support the voltage during the grid faults.In this paper,a voltage control strategy to support the voltage in a distribution network is proposed by introducing three-port FMSS.The positive-negative-sequence compensation(PNSC)scheme is adopted to control the active and reactive current.This control scheme eliminates active power oscillations at the port of voltage sags and reduces coupling oscillations of other ports.Based on the characteristics of the voltage support under PNSC scheme,two voltage support strategies are proposed.A proportional-integral controller is introduced to provide the reactive power references,which eliminates the errors when estimating the grid voltage and impedance.A current limiting scheme is adopted to keep the port current in a safe range by adjusting the active and reactive power references.The voltage support strategies in two different voltage sags are simulated,and results show the feasibility and effectiveness of the proposed control strategies.

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.

The Mechanical Stabilization and Multi-States of the Stretched VWF A2 Domain

At the vascular injury sites,the ultra-large (UL) multimeric von willebrand Factor (VWF) is released in response to physiological and pathophysiological stimuli,and mediates platelet adhesion,aggregation,and cross-linking to maintain hemostasis.This UL-VWF is specifically cleaved by ADAMTS13(A Disintegrin And Metalloprotease with a ThromboSpondintype-1 motif,member 13)to prevent microvascular thrombosis.Each VWF monomer consists of five types of repeat domains in the order of D1-D2-D’-D3-A1-A2-A3-D4-C1-C2-C3-C4-C5-C6-CK,in which the A2 domain contains the ADAMTS13cleavage site(Tyr1605-Met1606),exposure of which requires mechanical or chemical stimuli.Under flows,fluid shear stress regulates VWF degradation and size distribution through opening the A2 domain and exposing its cleavage site for ADAMTS13.VWF A2 domain contains a C-terminal vicinal disulfide bond,a calcium binding sites,and a flexibleα4-less-loop.These unique structure features together make A2 more sensitive to mechanical signal than other VWF A subdomains,i.e.A1 and A3 domains.It is believed that A2 is first bound with and then cleaved by ADAMTS13,together with force-induced conformation transformation.To reveal molecular basis of this two-step model of VWF hydrolyzation by ADAMTS13,we here examined stretch-induced unfolding processes of VWF A2 domain in more detail by Steered molecular dynamics(SMD)simulations,with the use of crystal structure of VWF A2(PDB ID 3GXB),and observed that there were multiple quasi-stable conformations of stretched A2 until itsβ4-strand and a3-helix were pulled away the central hydrophobic core and the cleavage sites were fully accessible to solvent.Our MD simulation data showed that,in unfolding,at first,the cleavage site residue Tyr1605 was exposed partially and binding sites for Spacer domain of ADAMTS13 were exposed to a high level whenα6-helix was separated from A2 body;then,withβ6-strand and a5-helix been pulled away,the binding sites for Cysrich domain of ADAMTS13 was exposed completely while the exposure degree of Tyr1605 was not improved;further,separation ofβ5-strand andα4-less-loop made Tyr1605 and Met1606 and the respective binding sites for ADAMTS13 Spacer domain,Cys-rich domain,and Disintegrin-like domain be fully exposed to reach the optimal catalytic state;lastly,withβ4-strand separation,the cleavage sites and binding sites all were overstretched,leading to mismatch of ADAMTS13 and A2 conformation especially in the binding sites.This conformational mismatch may cause reduction of ADAMTS13 hydrolysis efficiency.Furthermore,the data of SMD simulations under constant forces demonstrated that,the stretched A2 conformation had different quasi-stable states,which all had the better mechanical stability within simulation time of 100 ns;and the conformational transformation from one state to another must overcome their respective potential barriers.The hydrolysis efficiency should depend on each state of the stretched A2 conformation,because of the exclusive matched-degree of A2 and ADMATS13.This computer prediction on the mechanical stability and multi-states of stretched A2 provides a novel insight into the mechano-chemical regulation on cleavage of A2 by ADAMTS13.It would be helpful for design of related drug targeting the binding sites on A2 and exosites on ADAMTS13 for the treatment of patients with acquired TTP.

On the use of residual dipolar couplings in multi-state structure calculation of two-domain proteins

Residual dipolar couplings(RDCs)are powerful nuclear magnetic resonance(NMR)probes for the structure calculation of biomacromolecules.Typically,an alignment tensor that defines the orientation of the entire molecule relative to the magnetic field is determined either before refinement of individual bond vectors or simultaneously with this refinement.For single-domain proteins this approach works well since all bond vectors can be described within the same coordinate frame,which is given by the alignment tensor.However,novel approaches are sought after for systems where no universal alignment tensor can be used.Here,we present an approach that can be applied to two-domain proteins that enables the calculation of multiple states within each domain as well as with respect to the relative positions of the two domains.

Dynamic Analysis of the Multi-state Reliability System with Priority Repair Discipline

This paper considers a multi-state repairable system that is composed of two classes of components,one of which has a priority for repair.First,we investigate the well-posedenss of the system by applying the operator semigroup theory.Then,using Greiner’s idea and the spectral properties of the corresponding operator,we obtain that the time-dependent solution of the system converges strongly to its steady-state solution.

多状态系统任务成功性仿真评估

针对多状态系统任务成功性评估问题,通过对系统运行规则进行分析,将其状态分为成功态和失败态两类。在计算系统状态持续时间分布和不同状态之间转移概率的基础上,利用半马尔可夫过程,建立系统运行的状态转移模型。通过分析使用、维修和保障资源约束的作用机理,对多状态系统任务成功性评估进行仿真设计。最后,结合示例验证了所提方法的可行性和有效性。

固定和切换拓扑下多智能体系统二分容错状态一致性研究

针对具有传感器故障的连续时间线性领导-跟随多智能体系统,研究了固定和切换拓扑下的二分容错状态一致性问题。首先,考虑不存在传感器故障的情况,提出了一种基于状态反馈的分布式二分一致性控制协议;其次,考虑存在传感器故障和状态不可测的情况,根据邻居交互信息构造新型分布式状态和故障观测器,并提出了基于输出反馈的分布式二分容错一致性控制协议;最后,进一步考虑通信拓扑发生切换的情况,提出了一种基于观测器的输出反馈二分容错一致性控制协议。理论分析和仿真算例均验证了不同情况下提出的分布式控制策略能够消除传感器故障带来的不良影响,同时,可实现预期的二分状态一致。

具有状态时滞和死区的非线性多智能体系统一致性

本文研究了一种带有状态时滞和死区的非线性多智能体系统一致性控制问题。由于系统受死区的影响,本文设计了相应多智能体系统一致性控制器进行处理,并对控制器进行分类分析。其次本文通过假设来使函数满足Lipschitz条件以处理系统中的非线性因素。最后通过构造李雅普诺夫函数和使用牛顿莱布尼茨定理解决时滞对系统的影响,再根据对称矩阵和Kronecker积的性质,及Schur补引理证明了多智能体系统一致性。

基于Luenberger观测器的不确定系统鲁棒状态反馈设计

针对不确定系统测量输出矩阵含有不确定参数的问题,提出一种基于新龙伯格(Luenberger)类型观测器的鲁棒状态反馈设计方法.首先,针对实践中状态变量难以测量的问题,通过观测状态的反馈来进行观测器设计,考虑不确定系统中测量输出矩阵含有不确定参数的情况,设计一种新龙伯格类型观测器;其次,在新龙伯格类型观测器基础上,结合多仿射表示和松弛变量框架,得到与李亚普诺夫函数相关的凸线性矩阵不等式(LMI)条件,进而对闭环系统进行基于线性矩阵不等式组的鲁棒稳定性分析;最后,通过实验检验上述条件的可行性,证明该方法的实用性和有效性.

基于有限状态机的数字化加工单元管控系统研究与应用

针对加工单元在数字化管控方面存在的设备管控与采集难、数字化程度低、系统响应慢等问题,以航天企业某单元为研究对象,分析单元业务流程,提出一种静态基础数据与动态业务数据分离的单元互联互通信息架构,构建面向硬件设备的数据通信模块,以实现多源异构设备的实时采集与封装。从响应式系统设计角度,引入有限状态机开展生产调度控制策略研究,分别以机器人和工件作为调度对象进行行为建模,结合面向对象方法,设计基于事件驱动的状态机程序结构。在此基础上,通过.NET相关技术,开发了面向单元全流程数字化管控的系统软件,并在生产现场验证了其有效性与可靠性。

基于多Agent的锂电池主动均衡策略控制仿真研究

【目的】针对锂电池的荷电状态均衡管理问题,提出一种基于多智能体的电池组荷电状态一致性均衡方案。【方法】首先,将多智能体控制策略引入电池管理的下垂控制中,实现了主动均衡电路拓扑下的自主均衡;其次,建立领航跟随者模型,利用参数已知的虚拟智能体使各个荷电状态不一致的电池的状态向其靠近,实现充放电模式下的荷电状态均衡;最后,对二阶多智能体荷电状态均衡控制策略进行仿真验证。【结果】实验结果表明,相比一阶均衡控制策略,自主均衡时间减少了43.02%,充电模式中均衡时间减少了16.13%,放电模式中均衡时间降低了32.90%。【结论】多智能体系统在电池的均衡管理中能够实现荷电状态的均衡,有效地降低了锂电池荷电状态到达一致性的收敛时间。

Extremely hidden multi-stability in a class of two-dimensional maps with a cosine memristor

We present a class of two-dimensional memristive maps with a cosine memristor. The memristive maps do not have any fixed points, so they belong to the category of nonlinear maps with hidden attractors. The rich dynamical behaviors of these maps are studied and investigated using different numerical tools, including phase portrait, basins of attraction,bifurcation diagram, and Lyapunov exponents. The two-parameter bifurcation analysis of the memristive map is carried out to reveal the bifurcation mechanism of its dynamical behaviors. Based on our extensive simulation studies, the proposed memristive maps can produce hidden periodic, chaotic, and hyper-chaotic attractors, exhibiting extremely hidden multistability, namely the coexistence of infinite hidden attractors, which was rarely observed in memristive maps. Potentially,this work can be used for some real applications in secure communication, such as data and image encryptions.