Course Description:
This course provides the concepts and methods of prognostics and health management (PHM) of engineering system, which bright insights into data-driven intelligent fault diagnosis and prognosis technologies. A variety of tools and techniques for developing health management and monitoring of components and systems will be discussed. Topics cover signal analysis and processing, feature extraction, machine learning methods for fault diagnosis, data-driven prognostics models and remaining useful life prediction. The course presents students to understand state-of-the-art health monitoring methods and explore applications in various engineering systems.
Course objectives:
1. Introduce to students the fundamental theories and cutting-edge methods on fault diagnosis and prognosis of engineering systems.
2. Introduce multi-domain signal processing and feature extraction, intelligent diagnosis models, machine learning methods, and remaining useful life prediction approaches.
3. Train students to conduct experimental demonstrations, explore application cases and test the PHM methods.
References:
1. Yaguo Lei, Naipeng Li, Xiang Li. Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems. Springer, 1st ed. 2023 Edition. ISBN-10: 9811691304
2. Nam-Ho Kim, Dawn An, Joo-Ho Choi. Prognostics and Health Management of Engineering Systems: An Introduction Springer, 1st ed. 2017 Edition. ISBN-10: 3319447408
3. Amiya R Mohanty. Machinery Condition Monitoring Principles and Practices. CRC Press, 2014. ISBN-10: 1466593040
- Teacher: zxyang, Yang Zhi Xin