Condition-Based Maintenance and Residual Life Prediction - Hardcover

Über die Autorin bzw. den Autor

Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, six chapters, and 100 papers in various peer-reviewed international journals and conferences. Additionally, he serves as an editor and mentor and is currently working on four industry-sponsored projects.

Davinder Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, India. He has published over 25 papers in various international journals and conferences and serves as a mentor for graduate and post-graduate students. His main research areas include production and industrial engineering, manufacturing technology, and innovation management.

Kanwal Jit Singh, PhD, is an associate professor in the Department of Mechanical Engineering, Guru Kashi University, India. He has published over 50 papers in various international journals and conferences and serves as a mentor to graduate and post-graduate students. His main research areas include production and industrial engineering, and ultrasonic machining.

Harleen Kaur, PhD, is an industry professional with over 11 years of experience currently working with Dr. Singh on an industry-sponsored project. She has published more than ten research papers in various international journals and conferences, three book chapters, and 20 books with international publishers.

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Condition-Based Maintenance and Residual Life Prediction is essential for those looking to effectively implement condition-based maintenance strategies and enhance fault detection through a comprehensive understanding of vibration data analysis and residual life prediction, addressing key challenges in the field.

Issues related to condition-based maintenance include its high initial cost, new techniques that can be difficult to implement due to resistance, older equipment that can be difficult to retrofit with sensors and monitoring equipment, and difficult-to-access equipment during production that is difficult to spot-measure. Keeping the above issues in mind, a general handbook for condition-based maintenance and residual life prediction is required to carry out in fault detection.

Condition-Based Maintenance and Residual Life Prediction aims to develop, analyze, and model condition-based maintenance and residual life prediction through vibration data. The analysis of vibration responses will aid in developing a fault detection system. The sources of vibration may be due to the presence of different types of defects, such as cracks in the shaft, a bent shaft, or misalignment of shafts. This will give designers a diagnostic tool for predicting the trends of vibration conditions, leading to early fault detection. The devised tool will be capable of quantifying the amplitude of vibration based on the severity of defects. With the features available in the devised diagnostic tool, the proposed model can be used for design, predictive maintenance, and condition-based maintenance.