Predicting Equipment Failure in Manufacturing Plants: An AI-driven Maintenance Strategy

Abstract

Failure of equipment can lead to costly repairs and downtime for manufacturing facilities. Traditional maintenance techniques can result in unexpected failures, which can cause revenue losses and production delays. Artificial Intelligence (AI)-driven methods use machine learning technologies to predict equipment malfunctions before they happen, which has gained widespread interest. Artificial intelligence-based maintenance strategies can help improve the reliability of equipment and reduce the cost of repairs in manufacturing facilities. This study explores the use of Kaggle's predictive maintenance dataset to predict equipment failure in a manufacturing plant. The research involves collecting and evaluating data, selecting machine learning models, and evaluating metrics.  The findings of this study revealed the application of AI-driven maintenance techniques to predict equipment failure, highlighting their potential to improve the efficiency of the manufacturing process and reduce costs. This study serves as a valuable contribution to the field of predictive maintenance and provides relevant implications for the industry. The paper explores the application of AI-based maintenance strategies to predict the failure of equipment in manufacturing facilities. The study utilizes the Kaggle dataset, which contains machine sensor readings and process variables from a manufacturing facility that processes batches of a specific product. The research methodology includes data gathering, engineering, and model selection. It also compared the effectiveness of AI-driven and traditional maintenance approaches. The results indicated that the former led to better predicted equipment failure rates and lower costs. The study's findings provide valuable insight into the subject of predictive maintenance and the implications it has for the manufacturing sector. It also offers future research directions.