An Intelligent Approach for Machine Downtime Prediction Using Ensembled Machine Learning Models

In industrial settings, unplanned machine downtime is a serious risk to profitability, operational effectiveness, and production. In order to predict machine breakdowns before they occur, this research offers a machine learning-based predictive maintenance framework that enables early prediction of machine downtime. The research is carried out using recorded data sets of industrial machines that operate according to various factors or reasons for downtime. Based on these values, prediction of downtime is possible. To guarantee data quality and consistency, several preprocessing techniques, such as imputation and normalization, were used on a dataset of 2,500 records and 16 features, ranging from hydraulic pressure and temperature to spindle vibration and torque. A variety of machine learning models, such as Random Forest, Support Vector Machines (SVM), LightGBM, XGBoost, and Gradient Boosting, were created and assessed. Although models such as SVM performed at a relatively moderate level, LightGBM and Gradient Boosting performed better than others in terms of prediction performance, with test accuracies surpassing 97%. The efficiency of machine learning in shifting from reactive to