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This research evaluates the effectiveness of various machine learning models using real-world industrial datasets. By comparing these methodologies, we aim to provide a comprehensive framework that minimizes operational downtime and enhances safety, ensuring that industrial systems remain functional and cost-effective through data-driven insights.