Predictive maintenance simulation of a chemical plant in Cameroon

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 series-parallel 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.