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Integrating Machine Learning With Industrial Automation for Enhanced Predictive Maintenance
Abstract
The integration of machine learning with industrial automation is transforming the landscape of predictive maintenance, a critical aspect of modern manufacturing and industrial operations. This research explores the synergies between machine learning algorithms and industrial automation systems. Predictive maintenance leverages data-driven insights to anticipate equipment failures, thereby reducing downtime, optimizing maintenance schedules, and enhancing operational efficiency. The core of the research focuses on the application of machine learning techniques to predictive maintenance. The paper outlines the processes of data collection, feature extraction, model training, and validation, highlighting the challenges and solutions associated with each step. The work discusses industrial applications based on machine learning for predictive maintenance. Issues like data integration, scalability and security are discussed along with strategies to overcome the challenges.
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