Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
Herausgeber: Kumar Tyagi, Amit; Soni, Gulshan; Tiwari, Shrikant
Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
Herausgeber: Kumar Tyagi, Amit; Soni, Gulshan; Tiwari, Shrikant
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Data Analytics and Artificial Intelligence (AI) play an important role in Predictive Maintenance (PdM) within the manufacturing industry. 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.
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Data Analytics and Artificial Intelligence (AI) play an important role in Predictive Maintenance (PdM) within the manufacturing industry. 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.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 400
- Erscheinungstermin: 23. Oktober 2024
- Englisch
- Abmessung: 234mm x 156mm x 24mm
- Gewicht: 762g
- ISBN-13: 9781032769523
- ISBN-10: 1032769521
- Artikelnr.: 70676777
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 400
- Erscheinungstermin: 23. Oktober 2024
- Englisch
- Abmessung: 234mm x 156mm x 24mm
- Gewicht: 762g
- ISBN-13: 9781032769523
- ISBN-10: 1032769521
- Artikelnr.: 70676777
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
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.
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.
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.
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.