Machine Learning for Predictive Maintenance in Industrial Systems

Priti Kandewar, Angeline Christobel Y. In: Smart Engineering Systems Using Artificial Intelligence and Emerging Technologies — ISBN: 978-81-69935-29-6. Pages: 63 - 83

of key patterns, attributes, and insights from visual information. In contrast to traditional image-processing techniques that rely significantly on manually crafted features, deep learning models possess the capability to autonomously acquire hierarchical representations directly from photos and videos. This section elucidates the core tenets and significant utilisations of deep learning within the realms of computer vision and image analysis. It addresses picture capture, preprocessing, feature representation, Convolutional Neural Networks (CNNs), transfer learning, object identification, image segmentation, image classification, facial recognition, video analytics, and generative models. Sophisticated frameworks including ResNet, EfficientNet, Vision Transformers, Generative Adversarial Networks, autoencoders, and transformer-centric vision models are likewise examined. The section emphasises pragmatic uses in healthcare, self-driving vehicles, production, farming, monitoring, remote observation, intelligent urban areas, and industrial automation. Additionally, issues concerning data accessibility, computational intricacy, interpretability, confidentiality, prejudice, resilience, and immediate implementation are explored. The chapter wraps up by highlighting prospective research avenues, such as multimodal vision systems, edge AI, foundational models, interpretable computer vision, synthetic data creation, and human-centric visual intelligence.