NEURAL NETWORK ARCHITECTURES FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL APPLICATIONS
Keywords:
neural networks, predictive maintenance, industrial applications, convolutional neural networks, recurrent neural networks, transfer learning, data augmentationAbstract
Predictive maintenance has emerged as a critical approach in industrial applications to minimize downtime, reduce costs, and enhance equipment longevity. Neural network architectures, particularly deep learning models, have demonstrated remarkable potential in analyzing complex patterns and predicting equipment failures before they occur. This paper explores state-of-the-art neural network architectures employed in predictive maintenance, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. Key use cases in industries such as manufacturing, energy, and transportation are analyzed to illustrate the practical applications of these models. The study also addresses challenges such as imbalanced datasets, feature engineering, and computational resource requirements, proposing solutions like transfer learning, data augmentation, and model optimization techniques. By leveraging advanced neural network architectures, predictive maintenance systems can achieve higher accuracy and reliability, paving the way for smarter industrial operations.
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