Incorporating deep learning data analytics techniques in the optimisation of capacitated planned maintenance

Manufacturing systems must be supported by the availability of materials, a streamlined production process and prepared line to achieve target. In mass customization manufacturing system, number machines required for is relatively small. Conse-quently, maintenance on critical will impact this system most. Two types strategies are implemented: corrective preventive maintenance. The requires more resources since time cost repair breakdown machine higher due fatal failure. For management consider while binding still operational, it equipped with deep analysis demonstrating that fewer required. This paper discusses two analyses: accurate prediction machines' based Mean Time Between Failure (MTBF) data using learning analytics technique optimizing total in available capacitated time. findings results show proposed can increase MTBF accuracy up 66.12% reduce 4% compared original model.