•Epistemic uncertainty and CCFs are synthesized in reliability analysis of MSSs•D-S evidence theory is used to express the epistemic uncertainty in system•A modified β factor parametric model is ...introduced to model the multiple CCF groups•Developed method is shown to be efficient and practical.
With the increasing complexity and size of modern advanced engineering systems, the traditional reliability theory cannot characterize and quantify the complex characteristics of complex systems, such as multi-state properties, epistemic uncertainties, common cause failures (CCFs). This paper focuses on the reliability analysis of complex multi-state system (MSS) with epistemic uncertainty and CCFs. Based on the Bayesian network (BN) method for reliability analysis of MSS, the Dempster-Shafer (DS) evidence theory is used to express the epistemic uncertainty in system through the state space reconstruction of MSS, and an uncertain state used to express the epistemic uncertainty is introduced in the new state space. The integration of evidence theory with BN which called evidential network (EN) is achieved by adapting and updating the conditional probability tables (CPTs) into conditional mass tables (CMTs). When multiple CCF groups (CCFGs) are considered in complex redundant system, a modified β factor parametric model is introduced to model the CCF in system. An EN method is proposed for the reliability analysis and evaluation of complex MSSs in this paper. The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability. The presented method has high computational efficiency, and the computational accuracy is also verified.
•The problem is formulated as a Markov decision process with a mixed state space.•The deep reinforcement learning algorithm is customized to resolve the problem.•A postprocess is developed to ...facilitate the actor to search the optimal solution.•The experience replay and the target network are utilized for training the agent.
Selective maintenance, which aims to choose a subset of feasible maintenance actions to be performed for a repairable system with limited maintenance resources, has been extensively studied over the past decade. Most of the reported works on selective maintenance have been dedicated to maximizing the success of a single future mission. Cases of multiple consecutive missions, which are oftentimes encountered in engineering practices, have been rarely investigated to date. In this paper, a new selective maintenance optimization for multi-state systems that can execute multiple consecutive missions over a finite horizon is developed. The selective maintenance strategy can be dynamically optimized to maximize the expected number of future mission successes whenever the states and effective ages of the components become known at the end of the last mission. The dynamic optimization problem, which accounts for imperfect maintenance, is formulated as a discrete-time finite-horizon Markov decision process with a mixed integer-discrete-continuous state space. Based on the framework of actor-critic algorithms, a customized deep reinforcement learning method is put forth to overcome the “curse of dimensionality” and mitigate the uncountable state space. In our proposed method, a postprocess is developed for the actor to search the optimal maintenance actions in a large-scale discrete action space, whereas the techniques of the experience replay and the target network are utilized to facilitate the agent training. The performance of the proposed method is examined by an illustrative example and an engineering example of a coal transportation system.
•Proposes more robust and effective selective maintenance optimization model.•Captures the S-dependence between components in a selective maintenance setting, and models the two-way ...interactions.•Considers stochastic imperfect maintenance actions for MSS.•Models their probability of success as a function of the number of imperfect maintenance actions performed earlier.
This paper presents a selective maintenance optimization problem for complex systems composed of stochastically dependent components. The components of a complex system degrade during mission time, and their degradation states vary from perfect functioning to complete failure states. The degradation rate of each component not only depends on its intrinsic degradation but also on the state of other dependent components of the system. The proposed approach captures the two-way interactions between components through system performance rates and uses Monte Carlo simulation to compute the reliability of the system in the next operational mission. Different maintenance actions such as do-nothing, perfect, and stochastic imperfect maintenance are considered during the maintenance break to improve the reliability of the system. The selective maintenance bi-objective optimization problem is modelled considering both the expected value and variance of the system reliability as objective functions. Time and budget are considered as constraints for finding the optimal maintenance strategy. Two illustrative examples are provided for a better understanding of the proposed approach and for demonstrating its effectiveness.
Purpose: to present the national security environment from the perspective of the main assumptions formulated by Aristotle. This was he, who examined 158 constitutions of states, and he also ...undertook the work of describing the known regimes in detail. He can be considered the first thinker to approach the entire spectrum of political and social issues in a scientific manner.
Method: the research was conducted using the following general scientific and special methods: the historical method during the study of the examination of constitutions by Aristotle; the method of analysis and synthesis related to the functioning of the state by Aristotle, abstract-logical method – for formulating theoretical generalizations and research conclusions.
The results of the study: are related to Aristotleʼs indications, that a democratic system can be a matrix for other forms of government and of utmost importance is to preserve sustainability. The intention was not to develop or to reject the existing theories, but it was possible to formulate the assumption, that democracy, as we know it, has been a specific system whose durability depends on specific virtues and goods, most often called liberal.
Theoretical implications: the practitioners can learn from the paper, that following Aristotle, the virtue of moderation should be incorporated into the cross-section of liberal political values for the benefit of the sustainability of democracy and the citizens who participate in its achievements. In this respect, the relationship between politics and ethics was revealed, which is characteristic of the classically understood philosophy of politics.
Practical implications: the findings could serve as the research streams related to deeper analysis of the democratic systems, and the relations with the essence of the democracy of ancient Athens and looking for inspiration and parallel solutions for the political culture of current liberal democratic societies.
Papertype: theoretical.
•Multi-state system performing multi-attempt mission under random shocks is considered.•After each failed attempt the system, if survived, is repaired to ‘as good as new’ state.•The repair time ...depends on the system state before the repair.•The mission time is limited.•The optimal number of shocks after which any attempt is aborted is considered.
Research on mission abort strategies was mostly devoted to binary systems that can be only in two states, i.e., operable or failed. However, the real-world systems can often operate in intermediate states with different levels of performance. On the other hand, if a mission has been aborted and a system has been successfully rescued, at some instances, the next attempt can be activated, thus forming the multi-attempt framework. In this paper, the possibility of multiple attempts is considered for the first time for multistate systems. After each rescue, a system is repaired to ‘as good as new’ state. The repair time depends on its state before the repair. The objective is to maximize the probability of a mission completion within the fixed time deadline for systems operating in a random environment modeled by shocks. Each shock with a given probability results in a system's transition to the states with the lower values of performance. Mission abort is activated for each attempt when the number of experienced shocks exceeds a predetermined number. This number for each attempt should be determined to maximize the mission success probability. For the considered illustrative example, the detailed sensitivity analysis is performed and the relevant discussion is provided.
•A modified third-order quantized state system (MQSS3) simulation method is proposed.•An event-driven framework is developed for the co-simulation of EH-IES using MQSS3.•Better performance is ...achieved in simulations of EH-IES with continuous-discrete properties.•Detailed dynamics can be derived to support the precise operation analysis of EH-IES.
Effective simulation methods are becoming critically essential for the analysis of integrated energy systems (IESs) to reveal the interactions of multiple energy carriers. The incorporation of various energy technologies and numerous controllers make the IES a heterogeneous system, which poses new challenges to simulation methods. This paper focuses on the simulation of an IES with hybrid continuous-discrete properties and heterogeneous characteristics. First, a modified third-order quantized state system (MQSS3) method is proposed for the simulation of district heating systems (DHSs), in which quantized state system (QSS) and time-discretized integration are integrated to efficiently manage numerous discrete control actions. Second, an event-driven framework is established to integrate MQSS3 into the simulation of the electricity-heat integrated energy system (EH-IES). This framework enables the adoption of the most suitable models and algorithms for different systems to accommodate the heterogeneous properties of an IES. Case studies of an EH-IES with maximum 80% PV penetration and 210 buildings demonstrate that the dynamic interactions between the DHS and the power distribution network are accurately illustrated by the proposed simulation methods, in which MQSS3 indicates the highest simulation efficiency. It is also demonstrated in the simulation results that the flexibility from DHS can be utilized as demand-side resource to support the operation of power distribution network in aspects such as consuming the surplus PV generations.
At many instances, information on the state of a multistate system executing a mission is obtained via the costly inspections performed at discrete instants of time. Therefore, the corresponding ...cost-wise optimal mission abort policy that takes into account the state of a system should be designed accordingly. In this paper, we introduce an optimal mission abort policy that minimizes the expected costs due to inspections, mission failure and loss of a system. A system operates in a random environment modeled by a renewal process of shocks. With each shock, its state can deteriorate with certain probabilities that can eventually result in the total failure. The decision to abort or to continue operation depends on the number of experienced shocks and on the state of a system that is revealed only at inspections. The corresponding cost minimization problem is formulated and the necessary relationships for solving it are derived. Detailed numerical illustrations and discussions are presented.
Modern systems are increasingly under the threaten of disruptive events like earthquakes, floods and storms. Under real life scenarios, multi-state models are often used to describe the behaviors of ...the system exposed to disruptive events. This article develops a comprehensive resilience modeling and quantifying framework for a multi-state system in which the evolution of the performance level over time is described by a time-homogeneous Markov process. In order to characterize the different dimensions of the system resilience, four types of resilience metrics are proposed to describe the resistant, absorption, recovery, and overall resilience, where each type is divided into an inherent resilience metric and an acquired resilience metric. The theory of aggregated stochastic processes is applied to derive explicit formulas for the four types of resilience metrics. Meanwhile, simulation-based algorithms are proposed to verify the correctness of analytical formulas. They are first exploited to the resilience analysis of a nuclear power plant under the threat of earthquakes, and then used in a numerical example to illustrate the applicability of the proposed method in dealing with the state space explosion problem. The results show that the developed resilience modeling and quantifying framework is able to comprehensively describe the resilience of multi-state systems threatened by disruptive events, and further, some practical suggestions are given to the designers and operators of the system based on the results.
•A resilience model for multi-state systems based on Markov processes is developed.•Four types of resilience metrics are proposed, each including an inherent and an acquired.•Analytical formulas and simulation algorithms of resilience metrics are given.•Two examples are provided to illustrate the resilience model and metrics.
This paper studies the selective maintenance problem for multi-state systems with structural dependence. Each component can be in one of multiple working levels and several maintenance actions are ...possible to a component in a maintenance break. The components structurally form multiple hierarchical levels and dependence groups. A directed graph is used to represent the precedence relations of components in the system. A selective maintenance optimization model is developed to maximize the system reliability in the next mission under time and cost constraints. A backward search algorithm is used to determine the assembly sequence for a selective maintenance scenario. The maintenance model helps maintenance managers in determining the best combination of maintenance activities to maximize the probability of successfully completing the next mission. Examples showing the use of the proposed method are presented.
•A selective maintenance model for multi-state systems is proposed considering both economic and structural dependence.•Structural dependence is modeled as precedence relationship when disassembling components for maintenance.•Resources for disassembly and maintenance are evaluated using a backward search algorithm.•Maintenance strategies with and without structural dependence are analyzed.•Ignoring structural dependence may lead to over-estimation of system reliability.
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