scholarly journals A simulation approach on reliability assessment of complex system subject to stochastic degradation and random shock

2020 ◽  
Vol 22 (2) ◽  
pp. 370-379
Author(s):  
Mohammad Reza Pourhassan ◽  
Sadigh Raissi ◽  
Ashkan Hafezalkotob
Keyword(s):  
Complex System ◽  
Reliability Assessment ◽  
Simulation Approach ◽  
Random Shock

Research on Reliability Evaluation Methods of Composite Generation and Transmission System

2014 ◽  
Vol 1044-1045 ◽  
pp. 466-477
Author(s):  
De Hua Cai ◽  
Le Feng Cheng ◽  
Lin Lin Su ◽  
Li Gou Wang ◽  
Tao Yu
Keyword(s):  
Complex System ◽  
System Reliability ◽  
Reliability Assessment ◽  
Reliability Evaluation ◽  
Transmission System ◽  
Power System Reliability ◽  
Evaluation Indexes ◽  
Evaluation Algorithm ◽  
State Enumeration ◽  
Engineering Projects

The basic mathematical method for power system reliability evaluation was indicated, the simple and complex system was respectively introduced, several methods of reliability analysis of simple was given in this paper, and the reliability evaluation indexes system and methods of composite generation and transmission system was analyzed, then focused on state enumeration method applied in the reliability assessment of system, the detailed example analysis was given and the adequacy reliability indexes were computed in detail. Finally, the current reliability evaluation and status of composite generation and transmission system was summarized simply and its reliability evaluation algorithm steps applied in current actual engineering projects was given.

scholarly journals Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041988108
Author(s):  
Hongping Yu ◽  
Mao Tang
Keyword(s):  
Fuzzy Numbers ◽  
Epistemic Uncertainty ◽  
Operating Time ◽  
Reliability Assessment ◽  
Cumulative Damage ◽  
Fuzzy Environment ◽  
Random Shock ◽  
Comparative Results ◽  
Random Shocks ◽  
Internal Degradation

Reliability assessment of multi-component systems under competing degradation and random shocks has been intensively investigated in recent years. In most cases, the parameters associated with competing degradation and random shocks are represented by crisp values. However, due to insufficient data and vague judgments from experts, it may produce epistemic uncertainty with those parameters and they are befitting to be described as fuzzy numbers. In this article, the internal degradation is treated as a continuous monotonically increasing random process with respect to operating time, whereas the amount of cumulative damage produced by each external random shock is modeled by a geometric process. As components in a system suffer the same environmental condition, an external random shock will produce different amounts of cumulative damage to each component simultaneously. Each component fails when either the internal degradation or cumulative damage from the random shocks, whichever comes first, exceeds its corresponding random thresholds. Moreover, the parameters associated with the internal degradation and the random shocks are represented by triangular fuzzy numbers. The fuzzy reliability functions of components and the entire system are evaluated by a set of optimization models. A multi-component system, together with some comparative results, is presented to illustrate the implementation of the proposed method.

A Novelty Model for Reliability Assessment of Complex System

2014 ◽  
Vol 571-572 ◽  
pp. 118-123
Author(s):  
Zong Run Yin ◽  
Dong Su ◽  
Jun Shan Li
Keyword(s):  
Complex System ◽  
Information Fusion ◽  
Bayesian Method ◽  
Reliability Assessment ◽  
Reliability Model ◽  
Source Information ◽  
Component Reliability ◽  
Reliability Parameters ◽  
System A ◽  
Go Methodology

Aiming at the difficulty in reliability assessment of complex system. A novelty model based on Bayesian method and GO methodology is proposed. Bayesian method is adopted for multi-source information fusion to build the component reliability model, and then GO methodology is utilized to integrate the component reliability parameters and form the reliability model of the system. At last, an instance of reliability assessment for complex electronic equipment is given to show the effectiveness of the model. Result shows that, this method take advantage of Bayesian method and GO methodology, it provide useful reference for relative applications.

scholarly journals Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of Species

2021 ◽  
Vol 5 (2) ◽  
pp. 37-49
Author(s):  
João Bioco ◽  
Paula Prata ◽  
Fernando Cánovas ◽  
Paulo Fazendeiro
Keyword(s):  
Complex System ◽  
Spatial Distribution ◽  
Life Cycle ◽  
Point Of View ◽  
Emergent Behavior ◽  
Model Parameters ◽  
Simulation Approach ◽  
Agent Based Model ◽  
Agent Based ◽  
Environment Variables

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.

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