<![CDATA[Predictive maintenance has become a critical strategy in modern manufacturing to reduce downtime, optimize operational efficiency, and minimize maintenance costs. Traditional approaches, such as rule-based and statistical methods, often fail to detect complex patterns and early signs of system failures. This paper explores the application of deep learning-based anomaly detection techniques to enhance predictive maintenance in manufacturing. Specifically, we investigate the use of autoencoders, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for identifying anomalies in sensor data collected from industrial equipment. Our proposed framework enables early fault detection by learning complex temporal and spatial patterns in machinery behavior. Experimental results demonstrate that deep learning models significantly improve anomaly detection accuracy compared to conventional methods, thereby facilitating timely maintenance interventions and reducing unexpected failures. The findings highlight the potential of deep learning in revolutionizing predictive maintenance, ensuring higher reliability and efficiency in manufacturing systems.]]>
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