Integration of Big Data and Machine Learning Solutions as Preventive Maintenance Strategies to Improve Manufacturing Productivity

Traditional maintenance strategies such as preventive and reactive maintenance are not practical considering the consistently scaling and expansion of the production line. In this paper, we examined the implementation of predictive maintenance strategies enabled with Big Data and machine learning technique to monitor and predict the health of typical manufacturing station such as 3-axis Computer Numerical Control (CNC) milling machines. The developed predictive maintenance model can be used to analyse the manufacturing data in real-time and assist scheduling advance maintenance under optimized scheduling, minimizing unforeseen production downtime due to the unscheduled maintenance and subsequently enhances manufacturing productivity. The outcome of this study demonstrates the successful implementation of the developed model and highlights the practical usability of the proposed solutions to improve the productivity of the manufacturing processes through mitigation of possible production loss predicted by the model.