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Full Description
Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.
Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning.
Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
Contents
1. Introduction to Machine Learning Fundamentals. 2. AI Applications in Production. 3. Data Analytics and Artificial Intelligence for Predictive Maintenance in Manufacturing. 4. Scalability and Deployment of Emerging Technologies in Predictive Maintenance. 5. AI Models for Predictive Maintenance. 6. Role of Machine Learning and Deep Learning Models for Predictive Maintenance. 7. Data Analytics and AI for Predictive Maintenance in Pharmaceutical Manufacturing. 8. Real-Time Violence Detection in Video Streams: Exploiting ResNet-50 for Enhanced Accuracy. 9. The Analytics Advantage: Sculpting Tomorrow's Decisions Today. 10. Using Ensemble Model to Reduce Downtime in Manufacturing Industry: An Advanced Diagnostic Framework for Early Failure Detection. 11. Use Cases of Digital Twin in Smart Manufacturing. 12. Data Analytics and Visualization in Smart Manufacturing Using AI-Based Digital Twins. 13. Business Analytics, Business Intelligence, and Paradigm Shift in Organizational Structure. 14. Applications of Human Computer Interaction, Explainable Artificial Intelligence and Conversational Artificial Intelligence in Real-Life Sectors. 15. AI for Industry 4.0 with Real-World Problems. 16. Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare. 17. Advancing IoT Anomaly Detection through Dynamic Learning.
