scholarly journals An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent

Author(s):  
Qiang Feng ◽  
Songjie Li ◽  
Bo Sun

According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules) based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negotiation and independent negotiation is established, and the heuristic rules applied to global and local maintenance decision making is proposed. Based on Contract Net Protocol and the heuristic rules, the maintenance decision making algorithm is put forward. Finally, a fleet consisting of 10 aircrafts on a 3-wave continuous mission is illustrated to verify this method. Simulation results indicate that this method can improve the availability of the fleet, meet mission demands, rationalize the utilization of support resources and provide support for online maintenance decision making among a mission oriented fleet.

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5948
Author(s):  
Renxi Gong ◽  
Siqiang Li ◽  
Weiyu Peng

Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which includes the consideration of technology, cost-effectiveness, and security, should be created, taking into account current research findings. In order to address the uncertainty of maintenance strategy selection, this paper proposes a maintenance decision-making model composed of cloud and vector space models. The optimal maintenance strategy is selected in a multivariate assessment system. Cloud models allow for the expression of natural language evaluation information and are used to transform qualitative concepts into quantitative expressions. The subjective and objective weights of the evaluation index are derived from the analytic hierarchy process and the grey relational analysis method, respectively. The kernel vector space model is then used to select the best maintenance strategy through the close degree calculation. Finally, an optimal maintenance strategy is determined. A comparison and analysis of three different representative maintenance strategies resulted in the following findings: The proposed model is effective; it provides a new decision-making method for power transformer maintenance decision-making; it is simple, practical, and easy to combine with the traditional state assessment method, and thus should play a role in transformer fault diagnosis.


Author(s):  
Oussama Kebir ◽  
Issam Nouaouri ◽  
Mouna Belhadj ◽  
Lamjed Bensaid

The rise of terrorism over the past decade did not only hinder the development of some countries, but also it continues to destroy humanity. To face this concept of an emerging crisis, every country and every citizen is responsible for the fight against terrorism. As conventional plans became useless against terrorism, governments are required to establish innovative concepts and technologies to support units in this asymmetric war. In this paper, we propose a new multi-agent model for counter-terrorism characterized by a methodical process and a flexibility to handle different contingency scenarios. The division of labour in our multi-agent model improves decision making and the structuring of organisational plans.


Author(s):  
QINGBIN YUAN ◽  
QINGFENG WANG ◽  
JINJI GAO

Downtime of rotating equipment in large petrochemical plants often led to serious or even disastrous safety and environmental accidents, which generally stem from inadequate maintenance or incapability of failure prediction. In order to allocate maintenance resources rationally and improve the reliability, availability and safety of equipment, a kind of risk- and condition-based maintenance decision-making and task optimizing system for rotating equipment in large petrochemical plants is established in this paper. Using real-time database, web service and service-oriented architecture (SOA), a risk- and condition-based maintenance decision-making system architecture is developed to provide a unified data structure and man–machine interface, which integrates reliability-centered maintenance (RCM), condition monitoring system (CMS) and manufacturing executive system (MES) together. Risk assessment and condition monitoring technology is applied to form maintenance decision making, such as to determine the priority maintenance level, to optimize maintenance content, and to determine the right maintenance time. Based on the decision-making system, the risk rank and degradation trend of failure characteristics are used to support the decision making and to optimize maintenance tasks. The result of an engineering case shows that the maintenance decision-making based on the risk assessment and condition monitoring can lower the operational risk while enhancing the reliability, availability and safety.


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