Utilizing Machine Learning Techniques for Enhanced Predictive Maintenance in the Manufacturing Sector

The contributions of machine learning algorithms to predictive maintenance systems, such as classification, regression, and anomaly detection, are investigated to clarify their functions in forecasting equipment malfunctions and optimizing maintenance schedules. Notwithstanding the potential for profound change, specific obstacles including the quality of data, the ease of interpretation, and the financial implications of implementation require careful deliberation. This paper highlights the concrete advantages gained from the implementation of predictive maintenance by employing case studies and numerical analysis. These benefits include decreased equipment outage, improved equipment reliability, and lower maintenance expenses. The results emphasize the critical nature of predictive maintenance in the manufacturing sector, shedding light on its potential to transform processes and guarantee ongoing efficiency. With the increasing adoption of digitalization and analytics across industries, predictive maintenance emerges as a critical element in enhancing manufacturing sector efficiency and competitiveness. This highlights the ongoing imperative for innovative and adaptable practices that strive for operational excellence. In performance comparisons for predictive maintenance, Neural Networks achieved the highest metrics with 96.3% accuracy, 94.8% precision, 97.2% recall, and a 95.9% F1 score. Random Forests followed with 94.5% accuracy, 92.7% precision, 95.8% recall, and a 94.2% F1 score. Decision Trees showed 92.1% accuracy, 89.3% precision, 94.2% recall, and 91.7% F1 score, while Logistic Regression had the lowest performance with 85.2% accuracy, 82.6% precision, 87.5% recall, and an 84.8% F1 score.