Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
CRC Press (Verlag)
978-1-032-76952-3 (ISBN)
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.
Amit Kumar Tyagi, PhD, is an Assistant Professor at the National Institute of Fashion Technology, New Delhi, India. Previously he was an Assistant Professor (Senior Grade 2) and Senior Researcher at Vellore Institute of Technology (VIT), Chennai, Tamilandu, India, from 2019 to 2022. He earned a PhD in 2018 at Pondicherry Central University, Puducherry, India. Dr. Tyagi joined the Lord Krishna College of Engineering, Ghaziabad (LKCE), from 2009 to 2010 and from 2012 to 2013. He was an Assistant Professor and Head of Research, Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India, from 2018 to 2019. His supervision experience includes more than ten master’s dissertations and one PhD thesis. He has contributed to several projects such as AARIN and P3-Block to address some of the open issues related to privacy breaches in vehicular applications (such as parking) and medical cyber physical systems (MCPS). He has published over 200 papers in refereed high-impact journals, conferences, and books, and some of his articles were awarded best paper awards. Dr. Tyagi has filed more than 25 patents (nationally and internationally) in the areas of deep learning, internet of things, cyber physical systems, and computer vision. He has edited more than 25 books for IET, Elsevier, Springer, CRC Press, etc. Also, Dr. Tyagi has authored four books on intelligent transportation systems, vehicular ad-hoc network, machine learning, and internet of things, with IET UK, Springer Germany, and BPB India. He is a winner of faculty research awards for 2020, 2021, and 2022 (three consecutive years) given by the Vellore Institute of Technology, Chennai, India. Recently, he was awarded the best paper award for a paper titled "A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient", in ICCSA 2020, Italy (Europe). His research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy. He is a regular member of ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, Universal Scientific Education and Research Network, CSI, and ISTE. Shrikant Tiwari, PhD, is an Associate Professor in the Department of Computer Science and Engineering (CSE), School of Computing Science and Engineering (SCSE) at Galgotias University, Greater Noida, Uttar Pradesh, India. Dr. Tiwari also is a Senior Member of IEEE. He earned a PhD in computer science and engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, India, in 2012 and an MTech in computer science and technology at the University of Mysore, India, in 2009. He has authored or co-authored more than 75 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics. Dr. Tiwari is a member of ACM, IET, FIETE, CSI, ISTE, IAENG, and SCIEI. He is also a guest editorial board member and a reviewer for many international journals of repute. Gulshan Soni, PhD, is an Associate Professor and Principal in Charge in the Computer Science Engineering Department at the School of Engineering and Information Technology, Mahaveer Academy of Technology and Science University (MATS University), Raipur, India. He earned a PhD at Pondicherry University, India, along with a BTech at the National Institute of Technology (NIT), Raipur, India, and an ME at the National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, India. His research interests include wireless sensor networks, wireless body area networks, MAC protocols, and routing protocols, as well as distributed computing. Dr. Soni has published extensively in reputable journals and presented at national and international conferences. With over eight years of teaching experience, he brings valuable expertise to both government and private academic institutions in India.
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.
| Erscheinungsdatum | 25.09.2024 |
|---|---|
| Reihe/Serie | Advances in Intelligent Decision-Making, Systems Engineering, and Project Management |
| Zusatzinfo | 27 Tables, black and white; 66 Line drawings, black and white; 1 Halftones, black and white; 67 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 930 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-76952-1 / 1032769521 |
| ISBN-13 | 978-1-032-76952-3 / 9781032769523 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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