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

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

Prof. Shailendra Shende1,

Assistant Professor, Department of Computer Science and Engineering, Government College of Engineering Chandrapur, Maharashtra, India

Janhvi Mohurle2, Aliya Kazi3, Viniya Jagtap4, Prachi Mesharam5, Arpita Gajghate6

Final year, Department of computer science and engineering, Government college of engineering, Chandrapur, Maharashtra, India.

Abstract: 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.

Key Words: Predictive Maintenance, Preventive

Maintenance, Neural Networks, downtime costs,

Maintenance cost, Machine learning, Internet of things, Random Forest Algorithm.