Predictive maintenance is a critical component of manufacturing operations, as unplanned downtime can result in significant losses in productivity and revenue. The goal of this research project is to investigate and develop AI-based solutions for predictive maintenance in manufacturing.

The project will involve the collection and analysis of large amounts of data from manufacturing operations, including data on equipment performance, maintenance history, and environmental conditions. The project will also involve the development of machine learning models and algorithms to predict equipment failures and maintenance needs.

Milestones for this project include the development of accurate and reliable AI-based predictive maintenance models, as well as the demonstration of their effectiveness in reducing downtime and improving productivity through pilot projects and field trials. Other milestones include the identification of best practices and standards for AI-based predictive maintenance, as well as the promotion of their adoption and integration into manufacturing operations.

The potential applications of this research are significant and include improving the efficiency, reliability, and safety of manufacturing operations. AI-based predictive maintenance can reduce the risk of equipment failures, increase the lifespan of equipment, and enable proactive maintenance, resulting in reduced downtime and maintenance costs. Additionally, the development of AI-based predictive maintenance solutions can promote innovation and job creation in the manufacturing industry and contribute to the transition towards Industry 4.0.