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Maintenance simulation of a chemical plant in Cameroon
(Işık Üniversitesi, 2023-11-22) Dibe, Sheilla Nji; Javadi, Sonya; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans Programı
In the field of manufacturing, remarkable strides have been made in the development of predictive maintenance strategies. The research has incorporated cutting-edge technological innovations, such as machine learning, artificial intelligence, and the Internet of Things (IoT). Manufacturers can now proactively identify and address equipment malfunctions. This research study employs a degradation model simulation to evaluate and predict the remaining lifespan of a rotating element bearing in the manufacturing assembly line of a chemical plant situated in Cameroon. Additionally, the objective of this study is to perform a comparative analysis that seeks to assess the impact of implementing preventive and predictive maintenance strategies on the overall operational efficiency of a manufacturing system characterized by a seriesparallel configuration. The study reveals that the predictive maintenance policy is more significant in manufacturing system where addressing system throughput or implementation cost. This highlights the enhanced efficiency and cost-effectiveness associated with predictive maintenance in manufacturing operations.