Predictive Maintanance: Machine Downtime Prediction Using Data Analytics & Machine Learning

Machine downtime significantly impacts production efficiency and operational costs in various industries. Predictive maintenance leverages data analytics and machine learning to forecast machine failures before they occur, reducing unexpected downtime and maintenance costs. This paper explores the implementation of machine downtime prediction models using historical machine data, sensor readings, and AI-based predictive techniques. The study compares various machine learning algorithms, including Regression models, Decision trees, Support vector machine and Random Forest Classifier to determine their efficacy in predicting machine failures.