Recent Advances in Condition Monitoring

Select Proceedings of Nccm 2024
Herausgegeben:Andhare, Atul; Ganesh Kumar, PVS

• Broschiertes Buch

Produktbeschreibung

This book presents select peer-reviewed proceedings of the National Conference on Condition Monitoring (NCCM 2024), organized by Visvesvaraya National Institute of Technology Nagpur in collaboration with the Condition Monitoring Society of India. It showcases the latest research and advancements in the field of condition monitoring and predictive maintenance. Various topics covered in this book include vibration monitoring, thermography, wear debris analysis, and other critical techniques employed in industry for condition monitoring. The book also emphasizes the implementation of advanced strategies for equipment reliability and the optimization of maintenance practices. This compilation is of significant value to researchers, professionals, and academicians working in the field of condition monitoring and predictive maintenance, offering insights into the most recent trends and developments shaping the industry.

Produktdetails

• Lecture Notes in Mechanical Engineering
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-96-5790-2
• Seitenzahl: 327
• Erscheinungstermin: 21. September 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9789819657902
• ISBN-10: 9819657903
• Artikelnr.: 73633419

Autorenporträt

Dr. Atul B. Andhare is a distinguished academic and researcher in the field of Mechanical Engineering, currently serving as the professor and head of the Mechanical Engineering Department at VNIT Nagpur, India. He completed his Ph.D. from IIT Bombay in 2007, following an M.Tech. from VRCE Nagpur in 1994 and a B.E. in Mechanical Engineering from Shivaji University, Kolhapur in 1987. With over 36 years of experience spanning industry, government institutions, and academia, Dr. Andhare has contributed extensively to the fields of machinery vibration, condition monitoring, and machine design. He has been instrumental in developing and teaching several key courses, including Machine Condition Monitoring, Design for Manufacturing and Assembly, and Machine Tool Design. PVS Ganesh Kumar is a distinguished engineer with a profound impact on the fields of vibration and noise, condition monitoring, and naval stealth technologies. He completed his B.E. in Mechanical Engineering with distinction from Andhra University in 1984 and later earned an M.Tech. in Computer Science and Engineering from IIT Bombay in 1989. Joining the Naval Science and Technological Laboratory (NSTL) as a Scientist B on April 30, 1985, Mr Ganesh Kumar dedicated over 38 years of service before his superannuation on June 30, 2023, retiring as an Outstanding Scientist and Associate Director at NSTL. Presently, he is the President of the Condition Monitoring Society of India.

Inhaltsangabe

Identification of Wear Particles obtained through Gearbox using Convolution Neural Network.- Fault detection of gearbox using sound intensity and sound pressure mapping.- Fault detection of gearbox using vibration and noise analysis.- Fault Detection of Industrial Air Blower Using Vibration Signal Analysis.- Structural Optimization of Diesel Genset Bracket Using FEA.- Optimizing Coal Mill downtime by early detection of Main Reducer Gearbox Foundation Deterioration.- Power Loss Prediction in Solar PV Modules Using Image Processing and Machine Learning.- Fault Initiation Identification in a Run to Fail Scenario of Bearing.- Qualitative and Quantitative Analysis of Accumulated Dust on Solar PV Modules.- Integrating Machine Learning With Empirical Mode Decomposition For Multiple Fault Diagnosis In Rotating Machinery.- Intelligent Fault Detection in Wind Turbine using Spectral Estimation Methods.- Optimisation of Sensors for Comprehensive Vibration Condition Monitoring at Large Sites.- Intelligent Oil Analysis: Predicting Maintenance Needs.- Detection Of Abnormality In Hydrostatic Lubrication System Through Average Shaft Centre Line Plot At Sinter Plant Waste Gas Fan.- Detection and monitoring of crack in a Grinding Mill Shell-A Case Study.- Minimal Redundancy Maximal Relevance (MRMR) Based Feature Ranking for Efficient Automated Fault Diagnosis of Rolling Element Bearing.