Fault diagnosis and prognosis of hybrid systems using bond graph models and computational intelligence

• Main Author: Yu, Ming
• Other Authors: Wang Dan Wei
• Format: Theses and Dissertations
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
• Online Access: https://hdl.handle.net/10356/50480
• Institution: Nanyang Technological University

Fault diagnosis and prognosis have received a lot of attention from the Prognostics and Health Management (PHM) society in the past decades. PHM society is a non-profit organization dedicated to the advancement of PHM as an engineering discipline. The society was incorporated in early 2009 as a New York corporation. The flagship event of the society is the Annual Conference of the PHM Society. As the complexity of industrial systems increases, fault diagnosis become more and more important since it is a crucial means to maintain system safety and reliability. Faults need to be detected close to their occurrence time, so that corrective actions can be taken in a timely manner, and thus avoid catastrophic consequences. These actions include resetting control parameters to compensate for the faults, or reconfiguring the system to minimize the effects of the fault.