Mashael Maashi, Nujood Alwhibi, Fatima Alamr, Rehab Alzahrani, Alanoud Alhamid and Nourah Altawallah, King Saud University, Kingdom of Saudi Arabia
When manufacturers equipment encounters an unexpected failure, or undergo unnecessary maintenance pre-scheduled plan, which happens for a total of millions of hours worldwide annually, this is time-consuming and costly. Predictive maintenance can help with the use of modern sensing technology and sophisticated data analytics to predict the maintenance required for machinery and devices. The demands of modern maintenance solutions have never been greater. The constant pressure to demonstrate enhanced cost-effectiveness return on investment and improve the competitiveness of the organization is always combined with the pressure of improving equipment productivity and keep machines running at the maximum output. In this paper, we propose maintenance prediction approach based on a machine learning technique namely random forest algorithm. The main focus is on the industrial duct fans as it is one of the most common equipment in most manufacturing industries. The experimental results show the accuracy, reliability of proposed Predictive Maintenance approach.
Predictive Maintenance, Maintenance, Random Forest, Duct Fan, Machine Learning & Artificial Intelligence