Intelligent Machinery Fault Diagnostics and Prognostics (eBook, ePUB)

The Future of Smart Manufacturing
Redaktion: Goyal, Deepam; Abou Houran, Mohamad; Sharma, Ankit

• Format: ePub

Produktbeschreibung

Intelligent Machinery Fault Diagnostics and Prognostics: The Future of Smart Manufacturing uses an interdisciplinary approach to provide a well-rounded understanding of smart manufacturing.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 1. Mai 2025
• Englisch
• ISBN-13: 9781040339558
• Artikelnr.: 73733816

Autorenporträt

Deepam Goyal holds a B.E. (Hons.), an M.E. with a Gold Medal, and a Ph.D. in Mechanical Engineering, all from Panjab University, Chandigarh, India. Currently, he serves as an Assistant Professor at Chitkara University, Punjab, India. During his doctoral studies, he was awarded the prestigious INSPIRE Fellowship by the Department of Science and Technology, India. In 2021, 2022 and 2023, Goyal was recognized among the top 2% of scientists globally by Stanford University - a testament to his impact in the field of mechanical engineering. Additionally, he has received numerous accolades, including the Gold MILCA Award from the Confederation of Indian Industries and the esteemed Dr. S. P. Luthra Memorial Gold Medal, along with a Certificate of Merit (Academic) from the National Institute of Technical Teachers' Training and Research, Chandigarh. Dr. Goyal's prolific research output includes authorship or co-authorship of one book, 28 SCI-indexed journal articles, over 55 conference papers, and eight book chapters. He holds six patents and a copyright, further showcasing his contributions to innovation and technology. As a sought-after speaker, he has delivered invited talks across India on topics such as MATLAB, LaTeX, machine learning, research writing techniques, and personality development at various prestigious private and government institutions. In addition to his scholarly contributions, Goyal serves as a reviewer board member of prominent international journals, including ASME Journal of Vibration and Acoustics, IEEE Transactions on Instrumentation & Measurement, IEEE Transactions on Industrial Electronics, IEEE Sensors Journal, Mechanical Systems & Signal Processing, Journal of Intelligent Manufacturing, Applied Acoustics, and Measurement. Goyal's research expertise spans condition monitoring, machine fault diagnostics, vibration analysis, optimization techniques, manufacturing technology, sensors, and artificial intelligence. Ankit Sharma serves as the Associate Director (Research) at the Centre for Research Impact and Outcome (CRIO), Chitkara University, Punjab, India. He earned his PhD in Mechanical Engineering from the prestigious Thapar Institute of Engineering and Technology, India. With over a decade of experience in academia, research, consultancy, training, and industry, Dr. Sharma has made significant contributions to his field. Dr. Sharma is actively involved in organizing the ICEMSMCI 2023 international conference, where he is the General Chair and Convenor. He has authored numerous publications in high-impact journals indexed by SCI, Scopus, and Web of Science, and has filed or published over 30 international patents. A dedicated scholar, he is the editor of several books on advanced manufacturing processes published by CRC Press, Taylor & Francis, and Elsevier, with more works in the pipeline. In addition, Dr. Sharma holds editorial positions for various international journals, including those published by Springer, Taylor & Francis, MDPI, and Chitkara University. Notably, he is the Managing Editor for AIP Conference Proceedings and serves as Series Editor for two prominent book series: Innovations in Smart Manufacturing for Long-Term Development and Growth (CRC Press) and Next Generation Manufacturing Processes (ASME). Dr. Sharma has been invited to deliver seminars and keynote talks at international conferences in the USA, China, and India. He has also received multiple Best Research Paper Awards and serves on the reviewer boards of several leading journals published by Elsevier, SAGE, Springer, Taylor & Francis, and MDPI. Mohamad Abou Houran earned his B.S. degree in Electrical Engineering from Damascus University, Syria, in 2008. He received his M.Sc. and Ph.D. degrees in Electrical Engineering from Xi'an Jiaotong University (XJTU), China, in 2014 and 2020, respectively. In 2020, he joined XJTU as part of the prestigious Young Talent Program (at the Assistant professor Level), where he currently serves as an Associate Professor. To date, he has authored over 50 research articles in SCI-indexed journals, including more than 20 papers in JCR Q1 journals such as Applied Energy, Energy, Renewable Energy, ECM, and IEEE Transactions. He has also presented his work at leading international conferences, including APEC (USA) and ECCE (China, Japan, Korea). His research has been supported as a Principal Investigator (PI) by grants from the National Natural Science Foundation of China (NSFC), the Chinese Ministry of Science and Technology, and key R&D projects from Shaanxi Province. During his PhD, he received several awards, including Outstanding PhD Student of XJTU and the Bronze Award at the "China Internet+" College Students Innovation and Entrepreneurship Competition in 2019. In February 2022, he was elevated to IEEE Senior Member status. He also serves as a reviewer for several prestigious journals published by IEEE, IOP, and Elsevier. His research interests focus on Power Electronics, particularly in areas such as Wireless Power Transfer (WPT), Machine Learning, and Renewable Energy Technologies.

Inhaltsangabe

1. Introduction to Fault Diagnostics and Prognostics: Direction Towards
Smart Manufacturing 2. Advanced Diagnostics and Prognostics of Gearbox
Faults in Smart Manufacturing: The Critical Role of Gearboxes 3. Vibration
and Support Vector Machine-Based Fault Diagnosis of Bevel Gearbox 4.
Identifying Inner-Race Fault of a Bearing Using Nonlinear Mode
Decomposition Technique Supported by Blind Source Separation Methods 5.
Detection and Classification of Low-Severity Stator Inter-Turn Faults in
Induction Motors Using Temporal Features: A Comparative Machine Learning
Approach 6. Feature Selection for Accurate Remaining Useful Life Prediction
of Bearing Using Machine Learning 7. Deep Learning and Statistical
Model-Based Data-Driven Intelligent Fault Prognostics of Rotary Machinery
8. Remaining Useful Life Prediction for Aircraft Structure: Towards a
Digital Twin Ecosystem 9. Free Vibration Control of Crack Curved Cracked
Simple Supported Beams using Fuzzy Logic Control with Particle Swarm
Optimization Tuning 10. Fault Diagnosis of Composite Mono Leaf Spring based
on Vibration Characteristics 11. Current Sensor Fault Tolerant Control for
Model Predictive Control of Induction Motor Drives