Deep Learning Models for Specific Industrial Problems using Predictive Maintenance

Deep learning has revolutionized various industries by enabling intelligent automation, predictive analytics, and enhanced decision-making. This paper explores the application of deep learning models in solving specific industrial problems across diverse domains such as manufacturing, healthcare, finance, and supply chain management. We analyse the effectiveness of convolutional neural networks (CNNs) in quality control, recurrent neural networks (RNNs) in predictive maintenance, and transformer-based models in financial forecasting. Additionally, we discuss challenges such as data scarcity, model interpretability, and computational costs, providing potential solutions and future research directions. The findings highlight the transformative impact of deep learning in industrial problem-solving and emphasize the need for industry-specific model optimization to achieve higher efficiency and accuracy. Keywords: Deep Learning, Industrial Applications, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer Models, Predictive Maintenance, Quality Control, Financial Forecasting, Supply Chain Optimization, Artificial Intelligence (AI).