How to Cite
Publication Dates
Abstract
Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relatively high for 2015-2016-2017. 2018 was utilized as a test year to assess the modeling work and validate the experimental results. In the second step, the artificial neural networks approach employs the python program as an AI, and the affinity ratio of real data using the performance measurement of the mean absolute error (MAE) was 0.005. To improve and reduce the value of absolute error, the genetic algorithm uses the python program and the convergence ratio became 0.001. It inferred that the algorithm is efficient in improving results. Thus, the genetic algorithm provided better results with fewer errors than the neural network alone. This concludes that the shown network has superior performance over others and the possibility of its long-term predictions for 2030. A Sing time series helped detect future cases by reading and inferring system data. The development of appropriate work plans will lower internal and external expenses of the systems and help integrate other capabilities by giving correct data sources of raw materials, costs, etc. To facilitate prediction for maintenance workers, an interface has been created that facilitates users to apply them using the python program represented by entering the times, an hour, a day, a month, a year, to predict the type and place of failure.
References
N. M. Durakbasa, G. Bas, L. Kräuter, and G. Poszvek, “The Assessment of Industrial Manufacturing Systems Towards Advanced Operations by Means of the Integrated Modeling Approach,” Vis. 2020 Innov. Dev. Sustain. Econ. Growth - Proc. 21st Int. Bus. Inf. Manag. Assoc. Conf. IBIMA 2013, vol. 2, no. January, pp. 1418–1426, 2013.
J. Rosén, “Development of Industrial Information Systems based on Standards,” 2010.
H. Pacaiova and J. Glatz, “Maintenance Management System,” MM Sci. J., vol. 2015, no. OCTOBER, pp. 665–669, 2015, doi: 10.17973/MMSJ.2015_10_201532.
F. Trojan and R. F. M. Marçal, “Proposal of Maintenance-types Classification to Clarify Maintenance Concepts in Production and Operations Management,” J. Bus. Econ., vol. 8, no. 7, pp. 560–572, 2017, doi: 10.15341/jbe(2155-7950)/07.08.2017/005.
K. B. Misra, “Handbook of Performability Engineering,” Handb. Performability Eng., no. August 2008, 2008, doi: 10.1007/978-1-84800-131-2.
B. Dergisi, “Atatürk Üniversitesi İ ktisadi ve İ dari Bilimler Dergisi, Cilt: 23, Say ı : 4, 2009 125,” pp. 125–134, 2009.
M. Naser, Determine the Optimum time for Preventive Maintenance Time in Al-Hilal Industrial Company LTd September 2010 . Journal of Economics and Administrative science 16(59):210 DOI:10.33095/jeas.v16i59.1512
K. S. Moghaddam, “Preventive Maintenance and Replacement Scheduling : Models and Algorithms,” Comput. Ind. Eng., no. November, pp. 1–85, 2008.
H. Ab-Samat, E. I. Basri, N. A. Harun, S. C. Wee, and S. Kamaruddin, “Preventive Maintenance Checklist Towards Effective Maintenance System: A Case Study in Semiconductor Industry,” Adv. Mater. Res., vol. 748, no. August, pp. 1208–1211, 2013, doi: 10.4028/www.scientific.net/AMR.748.1208.
I. Badi and A.Shetwan, Measuring Performance Indicator for Maintenance Work:A Case Study in the longitudinal Rolling Mill at the Libyan Steel Company ,,pp.147-165,2016.The Scientific Court Journal,the tenth year,Issue 16 September 2016
A. B. Adulghafour, “Developing of Reliability-Centered Maintenance Methodology in Second Power Plant of South Baghdad,” Eng. Technol. J., vol. 36, no. 8A, 2018, doi: 10.30684/etj.36.8a.3.
L. M. Dawood, “Study of Using Weighting Property Index for Selecting the Best Maintenance Management System (M.M.S.) at Power Plants,” Djes, vol. 11, no. 4, pp. 20–27, 2018, doi: 10.24237/djes.2018.11404.
Y. Ran, X. Zhou, P. Lin, Y. Wen, and R. Deng, “A Survey of Predictive Maintenance: Systems, Purposes and Approaches,” vol. XX, no. Xx, pp. 1–36, 2019, [Online]. Available: http://arxiv.org/abs/1912.07383.
A. Mohammed, A. Ghaithan, M. Al-Saleh, and K. Al-Ofi, “Reliability-based Preventive Maintenance Strategy of Truck Unloading systems,” Appl. Sci., vol. 10, no. 19, pp. 1–17, 2020, doi: 10.3390/app10196957.
A. Mansour and N. Makhoul, Restore Service in Electrical Power Distribution Systems to Increase their Reliability Using Genetic Algorithm, Damascus University Journal of Engineering Sciences., Vol. 28,no.2, December, pp.19,93-111, 2012.
R. H. Fouad and M. Samhouri, “A fuzzy logic Approach for Scheduling Preventive Maintenance in ERP System,” Int. Conf. Manag. Serv. Sci. MASS 2011, no. December, 2011, doi: 10.1109/ICMSS.2011.5999330.
D. Król, L. Madeyski, and N. T. Nguyen, “Preface,” Stud. Comput. Intell., vol. 642, no. February, pp. v–vii, 2016, doi: 10.1007/978-3-319-31277-4.
H. Javanmard and A. al W. Koraeizadeh, “Optimizing the Preventive Maintenance Scheduling by Genetic Algorithm Based on Cost and Rreliability in National Iranian Drilling Company,” J. Ind. Eng. Int., vol. 12, no. 4, pp. 509–516, 2016, doi: 10.1007/s40092-016-0155-9.
M. Gregor, M. Haluška, M. Fusko, and P. Grznár, “Model of Intelligent Maintenance Systems,” Ann. DAAAM Proc. Int. DAAAM Symp., vol. 2015-Janua, no. January, pp. 1097–1101, 2015, doi: 10.2507/26th.daaam.proceedings.154.
K. Alhamad, M. Alardhi, and A. Almazrouee, “Preventive MaintenanceSscheduling for Multicogeneration Plants with Production constraints Using Genetic Algorithms,” Adv. Oper. Res., vol. 2015, 2015, doi: 10.1155/2015/282178.
A. Holman Montiel, S. Fernando Martínez, and F. H. Martínez S, “Genetic Algorithm Used for Improving the Preventive Maintenance Processes for Electricity Distribution Companies,” Int. J. Appl. Eng. Res., vol. 12, no. 19, pp. 8120–8124, 2017.
M. Vannucci, V. Colla, S. Dettori, and V. Iannino, “Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems,” J. Intell. Syst., vol. 29, no. 1, pp. 409–422, 2020, doi: 10.1515/jisys-2016-0343.
X. Bampoula, G. Siaterlis, N. Nikolakis, and K. Alexopoulos, “A Deep learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders,” Sensors (Switzerland), vol. 21, no. 3, pp. 1–14, 2021, doi: 10.3390/s21030972.
T. Miller, “Explanation in Artificial Intelligence: Insights from the Social Sciences,” Artif. Intell., vol. 267, pp. 1–38, 2019, doi: 10.1016/j.artint.2018.07.007.
Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.