Publication Date:
2013-08-31
Description:
We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.
Keywords:
CYBERNETICS
Type:
NASA. Goddard Space Flight Center, The 1994 Goddard Conference on Space Applications of Artificial Intelligence; p 285-291
Format:
application/pdf
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