A high‐performance maintenance strategy for stochastic selective maintenance

2018 ◽  
Vol 31 (12) ◽  
Author(s):  
Jing Zhao ◽  
Jianqi Liu ◽  
Zhenting Zhao ◽  
Miao Xin ◽  
Yu Chen
Keyword(s):  
High Performance ◽  
Maintenance Strategy ◽  
Selective Maintenance

Prognosis Informed Design Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

scholarly journals STATE-ORIENTED MAINTENANCE STRATEGY FOR DETERIORATING SEGMENTAL LINING OF TUNNEL

2018 ◽  
Vol 24 (6) ◽  
pp. 469-480 ◽  
Author(s):  
Qing Ai ◽  
Yong Yuan
Keyword(s):  
High Performance ◽  
Principal Part ◽  
Tunnel Lining ◽  
The Other ◽  
Limited Resources ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Maintenance Costs ◽  
Degradation Rates ◽  
Service State

The lining of tunnel is the principal part in ensuring its usage for transportation or utilities. Maintenance during operation is the way to keep structure in the state of high performance. To consider the efficiency of maintenance within limited resources (maintenance cost), state-oriented maintenance (SOM) approach is proposed. The SOM approach takes service state of the tunnel lining as a levelled target which should be maintained by different degrees of repairing measures. The evolution of service state of tunnel lining is modelled as a non-stationary Gamma process in this investigation, as this process can characteristic the variations of degradation rates. Two cases of numerical analysis with SOM approach give their lifetime maintenance costs. Case one (MS-1) sets SOM strategy with two-levelled service state. The other case (MS-2) adopts SOM strategy with multi-levelled service state. The numerical results demonstrate that the later one would reduce maintenance cost during the whole service life of tunnel lining.

scholarly journals Optimal Selective Maintenance Decision-Making for Consecutive-Mission Systems with Variable Durations and Limited Maintenance Time

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Huiying Gao ◽  
Xiaoqiang Zhang ◽  
Xiaoqiang Yang ◽  
Bo Zheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Great Influence ◽  
Maintenance Strategy ◽  
Maintenance Time ◽  
Selective Maintenance ◽  
Maintenance Decision ◽  
Set Up ◽  
Maintenance Model ◽  
Repairable Components

Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

Prognosis Informed Stochastic Decision Making Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang ◽  
Janet M. Twomey
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Sign in / Sign up

Export Citation Format

Share Document