Abstract
Advancements of Industry 4.0 has revolutionized manufacturing operations, among them predictive maintenance (PdM) acts as one of the most demanding approaches. It effectively optimizes maintenance schedules and ensures efficient and uninterrupted work. Article provides a comprehensive literature review, offering insights into theoretical foundations, historical developments, and practical applications of predictive maintenance. The methodology section explains the research approach in detail, focusing on the development of a MATLAB-based code to generate the predictive model in accordance with the remaining useful life of the machine. Exploration into the application of PdM is made through the establishment of Bayesian Inference model informed by Pearson correlation analysis. This study underscores the possibilities of predictive analytics in enhancing operational accuracy and effectivity across various industries. As the demand for reliable manufacturing processes continues to grow, the findings of this research offer insights into the development of advanced PdM strategies and achievement of operational excellence in terms of smart manufacturing.