Reliability Analysis and Modeling for Complex Systems
- 1st Edition - September 1, 2025
- Imprint: Elsevier
- Editors: Seifedine Kadry, Shubham Mahajan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 5 5 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 5 6 - 0
Reliability Analysis and Modeling for Complex Systems is a crucial resource for engineers and technologists grappling with modern challenges. As technology advances and safety… Read more
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Reliability Analysis and Modeling for Complex Systems is a crucial resource for engineers and technologists grappling with modern challenges. As technology advances and safety concerns mount, the complexity of systems like autonomous vehicles and critical infrastructure demands innovative reliability assessment methods. This book bridges theory and practice, offering practical solutions for professionals navigating the intricate world of reliability engineering. Through real-world case studies and interdisciplinary insights, it equips readers to address the multifaceted challenges of ensuring dependability in today's interconnected technological landscape.
- Dives deeply into advanced probabilistic modeling and analysis techniques tailored for complex systems. This content addresses the needs of reliability engineers and researchers who seek to apply cutting-edge methods to their projects
- Includes interdisciplinary perspectives, such as human factors and cyber-physical systems, which is essential for addressing the real-world challenges the target audience faces. This approach will help readers tackle complex system reliability from multiple angles
- Real-world case studies that bridge theory and practice, helping practitioners and academics understand how to apply reliability analysis to complex systems in various domains
Reliability Engineers and Practitioners. Researchers and Academics in Reliability Engineering
1. Introduction to Reliability Analysis for Complex Systems
2. Probabilistic Modeling Techniques for Complex Systems
3. Bayesian Methods in Reliability Analysis
4. Reliability Assessment of Cyber-Physical Systems
5. Human Factors in Complex System Reliability
6. Reliability-Centered Maintenance for Complex Systems
7. Failure Mode and Effects Analysis (FMEA) for Complex Systems
8. Measuring Complexity of Engineering Systems
9. Case Studies in Reliability Analysis of Complex Systems
10. Reliability of Renewable Energy Systems
11. Reliability in Healthcare Systems
12. Reliability of Autonomous Vehicles
13. Future Trends in Complex System Reliability
2. Probabilistic Modeling Techniques for Complex Systems
3. Bayesian Methods in Reliability Analysis
4. Reliability Assessment of Cyber-Physical Systems
5. Human Factors in Complex System Reliability
6. Reliability-Centered Maintenance for Complex Systems
7. Failure Mode and Effects Analysis (FMEA) for Complex Systems
8. Measuring Complexity of Engineering Systems
9. Case Studies in Reliability Analysis of Complex Systems
10. Reliability of Renewable Energy Systems
11. Reliability in Healthcare Systems
12. Reliability of Autonomous Vehicles
13. Future Trends in Complex System Reliability
- Edition: 1
- Published: September 1, 2025
- Imprint: Elsevier
- Language: English
SK
Seifedine Kadry
Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.
Affiliations and expertise
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, NorwaySM
Shubham Mahajan
Dr. Shubham Mahajan, a distinguished member of prestigious organizations such as IEEE, ACM, and IAENG, boasts an impressive academic and professional background. He earned his B.Tech. degree from Baba Ghulam Shah Badshah University, his M.Tech. degree from Chandigarh University, and his Ph.D. degree from Shri Mata Vaishno Devi University (SMVDU) in Katra, India. Dr. Mahajan's commitment to academic excellence extends to his ongoing pursuit of a Post Doctoral degree in the Department of Applied Data Science at Noroff University College in Kristiansand, Norway.
Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 70 articles published in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.
In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.
Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.
Affiliations and expertise
Department of Applied Data Science, Noroff University College, Kristiansand, Norway