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Neural Networks for Predictive Maintenance: Advancing Efficiency and Reducing Downtime in Industry 4.0
Abstract
In the era of Industry 4.0, predictive maintenance is transforming equipment management by leveraging AI, IoT, and machine learning. This paper highlights the crucial role of neural networks in detecting anomalies, predicting failures, and optimizing maintenance. IoT sensor integration enables real-time monitoring, allowing AI models to analyze parameters such as vibration and temperature. Advanced architectures, including convolutional and recurrent neural networks, enhance predictive accuracy. This proactive approach reduces costs, minimizes downtime, and extends equipment lifespan. However, adoption faces challenges such as high initial costs, data quality issues, and cybersecurity risks. This chapter examines these challenges and explores emerging trends like hybrid neural networks and AI-driven automation, which improve scalability and reliability, enabling industries to transition toward more efficient and resilient maintenance strategies.
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