1st Edition
Data-Driven Fault Diagnosis A Machine Learning Approach for Industrial Components
1. Introduction
2. Fault Diagnosis of the Pelton Turbine
3. Fault Diagnosis of the Francis Turbine
4. Fault Diagnosis of the Centrifugal Pump
5. Fault Diagnosis of Bearing
6. The Future of Machine Learning in Fault Diagnosis
References
Index
Biography
Govind Vashishtha received a PhD degree in Mechanical Engineering from the Sant Longowal Institute of Engineering and Technology, Longowal, India, in 2022. He is currently working as a Visiting Professor at Wroclaw University of Science and Technology, Wroclaw, Poland. He has authored over 70 research papers in Science Citation Index (SCI) journals and has also edited one book. His name has appeared in the world’s top 2% scientist list published by Stanford University in 2023 and 2024. He is also serving as Associate Editor in Frontiers in Mechanical Engineering, Shock and Vibration, Measurement and Engineering, and Applications of Artificial Intelligence. He has two Indian patents. His H-index is 27 and he has more than 1800 citations to his credit. His current research includes fault diagnosis of mechanical components, vibration and acoustic signal processing, identification/measurement, defect prognosis, machine learning, and artificial intelligence.
