Lorem ipsum dolor sit amet consectetur adipisicing elit. Architecto, aliquam pariatur. Illum officiis nam dolore eaque nostrum tenetur necessitatibus, nobis exercitationem rem laborum odio molestiae doloribus perferendis, voluptas quia officia porro veniam aperiam recusandae, minima commodi?
Industrial heavy machinery is critical to operations across sectors such as manufacturing, mining, and energy. Unexpected machinery failures can lead to costly service interruptions and operational inefficiencies. This project aimed to develop a Predictive Maintenance System to proactively identify machinery at risk of imminent failure, optimize resource allocation, and improve overall equipment effectiveness (OEE).
By employing machine learning techniques and advanced analytics, the system provided actionable insights and reduced downtime while supporting better decision-making for maintenance planning.
