On the Use of Mean Residual Life as a Condition Index for Condition-Based Maintenance Decision-Making

2014 ◽  
Vol 44 (7) ◽  
pp. 877-893 ◽  
Author(s):  
Khac Tuan Huynh ◽  
Inmaculada Torres Castro ◽  
Anne Barros ◽  
Christophe Berenguer
Keyword(s):  
Decision Making ◽  
Residual Life ◽  
Condition Index ◽  
Condition Based Maintenance ◽  
Mean Residual Life ◽  
Maintenance Decision

scholarly journals On the Construction of Mean Residual Life for Maintenance Decision-Making

2012 ◽  
Vol 45 (20) ◽  
pp. 654-659 ◽  
Author(s):  
K.T. Huynh ◽  
I.T. Castro ◽  
A. Barros ◽  
C. Bérenguer
Keyword(s):  
Decision Making ◽  
Residual Life ◽  
Mean Residual Life ◽  
Maintenance Decision

scholarly journals Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5948
Author(s):  
Renxi Gong ◽  
Siqiang Li ◽  
Weiyu Peng
Keyword(s):  
Decision Making ◽  
Vector Space ◽  
Assessment System ◽  
Condition Based Maintenance ◽  
Power Transformers ◽  
Maintenance Strategy ◽  
Cloud Models ◽  
Optimal Maintenance ◽  
Maintenance Decision ◽  
Vector Space Models

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.

THE RESEARCH OF RISK- AND CONDITION-BASED MAINTENANCE DECISION-MAKING AND TASK OPTIMIZING SYSTEM FOR ROTATING EQUIPMENT IN LARGE PETROCHEMICAL PLANTS

2012 ◽  
Vol 19 (04) ◽  
pp. 1250017
Author(s):  
QINGBIN YUAN ◽  
QINGFENG WANG ◽  
JINJI GAO
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Condition Monitoring ◽  
Service Oriented Architecture ◽  
Condition Based Maintenance ◽  
Decision Making System ◽  
Maintenance Decision ◽  
Rotating Equipment ◽  
The Right ◽  
And Task

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.

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

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiang Feng ◽  
Songjie Li ◽  
Bo Sun
Keyword(s):  
Decision Making ◽  
Condition Based Maintenance ◽  
Heuristic Rules ◽  
Layer Model ◽  
Agent Model ◽  
Intelligent Maintenance ◽  
Support Resources ◽  
Multi Agent ◽  
Maintenance Decision ◽  
Global And Local

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.

scholarly journals Residual life-based importance measures for predictive maintenance decision-making

2021 ◽  
pp. 1748006X2110281
Author(s):  
Phuc Do ◽  
Christophe Bérenguer
Keyword(s):  
Decision Making ◽  
Residual Life ◽  
Life Time ◽  
Actual Condition ◽  
Importance Measure ◽  
Reliability Engineering ◽  
Economic Dimension ◽  
Decision Aiding ◽  
Maintenance Decision ◽  
System Life

Importance measures have been widely used as meaningful decision-aiding indicators in reliability engineering, risk management and maintenance optimization. However, few importance measures integrates the actual condition (working states or degradation levels) of components that dynamically evolves with time. This work develops a novel time-dependent importance measure defined as the capacity of a component (or group of components) to improve, when it is replaced, the system residual life. The proposed [Formula: see text] measure can help to better prioritize a component or group of components regarding to its improvement ability in the system life time while considering the actual conditions of all components of the system. The originality and complementarity of the proposed measure when compared to existing importance measures is also investigated. The proposed importance measure is then extended to integrate the economic dimension of the maintenance decision, through the maintenance costs, the benefit gained by the maintenance operations and as well as the economic dependence between components. It is finally shown how the proposed [Formula: see text] measure and its extension can “optimally” suggest a component or a group of several components for preventive maintenance decision-making, based on both the technical criterion (residual life of the system) and the economic aspects (benefit and costs). The use and the advantages of the proposed importance measure and its extension are illustrated on a four-component system.

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