Abstract
Predictive maintenance (PdM) is an essential strategy for enhancing the operational efficiency and longevity of industrial systems. Neural networks, a subset of artificial intelligence, have emerged as powerful tools for predictive maintenance, enabling accurate fault detection, equipment health monitoring, and failure prediction. This article explores the various neural network-based approaches for predictive maintenance in industrial settings, including the use of deep learning, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other machine learning techniques. Additionally, the article discusses the benefits, challenges, and future directions for the integration of neural networks in PdM systems.
other critical sectors
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