On a periodic maintenance problem

1983 ◽  
Vol 2 (2) ◽  
pp. 90-93 ◽  
Author(s):  
W.D Wei ◽  
C.L Liu
1989 ◽  
Vol 27 (1-5) ◽  
pp. 657-664 ◽  
Author(s):  
Al Mok ◽  
Louis Rosier ◽  
Igor Tulchinsky ◽  
Donald Varvel

2006 ◽  
Vol 172 (3) ◽  
pp. 783-797 ◽  
Author(s):  
Alexander Grigoriev ◽  
Joris van de Klundert ◽  
Frits C.R. Spieksma

2016 ◽  
Vol 252 (2) ◽  
pp. 385-396 ◽  
Author(s):  
Raca Todosijević ◽  
Rachid Benmansour ◽  
Saïd Hanafi ◽  
Nenad Mladenović ◽  
Abdelhakim Artiba

Author(s):  
Luis Eduardo Accordi Ferrari ◽  
Rodrigo Donni ◽  
Paulo Smith Schneider ◽  
Daniel Dall Onder dos Santos

Author(s):  
Divyansh Goel ◽  
◽  
Agam Agarwal ◽  
Rohit Rastogi ◽  
◽  
...  

2021 ◽  
Vol 11 (2) ◽  
pp. 574
Author(s):  
Rundong Yan ◽  
Sarah Dunnett

In order to improve the operation and maintenance (O&M) of offshore wind turbines, a new Petri net (PN)-based offshore wind turbine maintenance model is developed in this paper to simulate the O&M activities in an offshore wind farm. With the aid of the PN model developed, three new potential wind turbine maintenance strategies are studied. They are (1) carrying out periodic maintenance of the wind turbine components at different frequencies according to their specific reliability features; (2) conducting a full inspection of the entire wind turbine system following a major repair; and (3) equipping the wind turbine with a condition monitoring system (CMS) that has powerful fault detection capability. From the research results, it is found that periodic maintenance is essential, but in order to ensure that the turbine is operated economically, this maintenance needs to be carried out at an optimal frequency. Conducting a full inspection of the entire wind turbine system following a major repair enables efficient utilisation of the maintenance resources. If periodic maintenance is performed infrequently, this measure leads to less unexpected shutdowns, lower downtime, and lower maintenance costs. It has been shown that to install the wind turbine with a CMS is helpful to relieve the burden of periodic maintenance. Moreover, the higher the quality of the CMS, the more the downtime and maintenance costs can be reduced. However, the cost of the CMS needs to be considered, as a high cost may make the operation of the offshore wind turbine uneconomical.


2021 ◽  
Vol 11 (14) ◽  
pp. 6524
Author(s):  
Andrés Pérez-González ◽  
Álvaro Jaramillo-Duque ◽  
Juan Bernardo Cano-Quintero

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.


2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mustafa A. Qamhan ◽  
Ammar A. Qamhan ◽  
Ibrahim M. Al-Harkan ◽  
Yousef A. Alotaibi

An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.


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