Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/50480
Title: Fault diagnosis and prognosis of hybrid systems using bond graph models and computational intelligence
Authors: Yu, Ming.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2012
Source: Yu, M. (2012). Fault diagnosis and prognosis of hybrid systems using bond graph models and computational intelligence. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
URI: http://hdl.handle.net/10356/50480
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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