A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
Norshakirah Aziz, Emelia Akashah Patah Akhir, Izzatdin Abdul Aziz, Jafreezal Jaafar, Mohd Hilmi Hasan, Ahmad Naufal Che Abas
Abstract
Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a system assessed to be prone to breakdown. The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0 (AIM 4.0), which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs. AIM 4.0 utilizes three different ensemble machine learning methods, including Gradient Boost Machine (GBM), Light GBM, and XGBoost for prediction of machine failures. The machine learning methods stated are implemented to produce acceptable accuracy for the monitoring task as well as producing a prediction with a high confidence level.