Predictive Maintenance using Machine Learning: A Case Study in Manufacturing Management

• Published in: 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 872 - 876
• Main Authors: Satwaliya, Deepanshu Singh, Thethi, H. Pal, Dhyani, Anubhuti, Kiran, G. Ravi, Al-Taee, Mustafa, Alazzam, Malik Bader
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.05.2023
• Subjects: AI applications; Boosting; Costs; data-supported decision making; Deep learning; deep learning algorithms; digital twins; digitalization; engine failure prediction; Fourth Industrial Revolution; inventory management; machine learning; Machine learning algorithms; Manufacturing; manufacturing management; Prediction algorithms; predictive maintenance; Production management; robotics
• DOI: 10.1109/ICACITE57410.2023.10183012

Predictive maintenance has become an important area of focus for many manufacturers in recent years, as it allows for the proactive identification of equipment issues before they become critical. In this paper, we present a case study in the application of machine learning for predictive maintenance in a manufacturing management setting. Through the implementation of various algorithms such as random forests, gradient boosting, and deep learning, we show that machine learning can provide significant improvements in the accuracy of predicting equipment failures. The results of our study demonstrate that predictive maintenance using machine learning can reduce downtime and maintenance costs, while improving the overall efficiency of manufacturing operations. The findings of this case study can provide valuable insights for manufacturers looking to adopt predictive maintenance strategies and incorporate machine learning into their maintenance management processes.