A Fuzzy Logic Approach for Scheduling Preventive Maintenance in ERP System

Fouad, Rami H.; Samhouri, Murad

doi:10.1109/icmss.2011.5999330

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Genetic algorithms have been used to build optimization models, saving designers and analysts time and effort in reaching a final solution. Mansour & Makhoul (2012) [16] dealt with reaching a high-reliability electrical system that protects the electrical power's permanence. The different method was presented for service continuity in electricity distribution networks through the use of genetic algorithms raised reliable and improved performance have been the data used in the algorithm calculation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

Ahmed

Abdulateef

2023

alkej

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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.

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Section: Literature Reviewmentioning

confidence: 99%

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

Ahmed

Abdulateef

2023

alkej

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“…Planning procedures should use the following data: system structure, reliability, potential and limitations of the service system. Maintenance planning based on fuzzy logic was proposed in (Fouad, Samhouri, 2011). The combination of knowledge in the field of system reliability based on the RCM strategy and fuzzy logic allows you to adapt the maintenance strategy to the current and future condition of devices (Felecia, 2014).…”

Section: The System Of Exploitation Of Military Technique and Its Res...mentioning

confidence: 99%

Analysis and evaluation of the technical equipment maintenance system in the Polish Army

Pszczółkowski,

Goliasz

2023

SLW

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Utrzymanie wysokiego poziomu gotowości operacyjnej wojska wymaga zapewnienia stałej wysokiej gotowości technicznej sprzętu stanowiącego jego wyposażenie. Niezawodność urządzenia technicznego wynika z jego cech konstrukcyjnych i eksploatacyjnych, a w fazie eksploatacji jest zależna wyłącznie od sprawności działania systemu zaopatrzenia logistycznego, obsługiwania, w tym diagnozowania. Zasady realizacji tych procesów i funkcjonowania systemów determinuje przyjęta strategia eksploatacji sprzętu. W opracowaniu przedstawiono wyniki oceny funkcjonowania systemu obsługiwania sprzętu technicznego w wojsku polskim. Scharakteryzowano metody, procedury i narzędzia pozyskiwania danych dla realizacji postawionego celu, metody przetwarzania i opracowania danych oraz analizy uzyskanej informacji. Informacje i dane charakteryzujące funkcjonowanie systemu obsługiwania uzyskano w wyniku zastosowania techniki wywiadu z ekspertami dysponującymi wiedzą teoretyczną jak i praktyczną o funkcjonowaniu systemu w siłach zbrojnych, za pomocą techniki obserwacji, analizy dokumentacji eksploatacyjnej w formie papierowej, jak również zgromadzonej w systemach informatycznych. Podstawowe znaczenie w procesie badawczym miała metoda ankietowania dwóch grup ekspertów, uczestników kursu logistycznego związanych z realizacją i zarządzających eksploatacją sprzętu. Do opracowania uzyskanych wyników liczbowych i lingwistycznych wykorzystano narzędzia informatyczne w postaci MS Excel, MatLab i Statistica. W opracowaniu wyznaczono parametry statystyki opisowej, histogramy i rozkłady prawdopodobieństwa oraz wykorzystano logikę rozmytą. W wyniku przeprowadzonego badania ujawniono przyczyny aktualnie nie w pełni zadowalającego poziomu funkcjonowania systemu utrzymania stanu technicznego sprzętu oraz wskazano możliwe do zastosowania rozwiązania pozwalające na podniesienie sprawności i efektywności systemu, a tym samym gotowości technicznej sprzętu.

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“…And in ERP itself has a part of Computer Maintenance Management System (CMMS), where this module used to maintenance process of an infrastructure in the company, such as IT infrastructure or machines. [1][2] [3][4] [5] Maintenance process of infrastructure is inseparable from the solution recommendation process and prediction. [6] [7] [8] shows that if the prediction process in all fields needs to be done is no exception in the industry, [6] [7] [8] has proven that the use of solution recommendations has developed in the weather prediction, then [9] [10] the proven of prediction in manufacturing and healthcare field.…”

Section: Introductionmentioning

confidence: 99%

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

Sukmana

Rozi

2019

KINETIK

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Decision support system, basically used to help choosing some solution for stakeholde to take the best decision in manufacturer. In manufacturer company using Enterprise Resource System (ERP) that has Work Oder (WO) modul as request maintenance from user. But many of data from WO still didn’t use to help decision making and only as warehouse data about infrastructure maintenance from last time. Because that, author use that data to help technician to take decision making by using association rule as pattern processing. This is because WO has unique pattern that has problem (p), symptom (s), and root cause (r). Previous research (Sukmana, Rozi, 2017) was proved if association rule can use to help people to take decison making, it is just involved two variable, that is symptom (s), root cause (r) and using apriori algorithm as association rule. And focussing in this research is using that three variable and eclat algorithm as association rule methode. Result of this research has purpose to take the best pattern when using eclat algorithm.

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A Fuzzy Logic Approach for Scheduling Preventive Maintenance in ERP System

Cited by 4 publications

References 12 publications

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

Analysis and evaluation of the technical equipment maintenance system in the Polish Army

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

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