Neural approximators for fault detection of actuators in the presence of friction: The case of the DAMADICS benchmark problem

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 36, Issue 5, 2003, Pages 963-968

  Papadimitropoulos, A A ^a   Rovithakls, G A ^b   Parisini, T ^c  

^a UNIVERSITY OF GENOA   (Italy)

^b ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^c UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Actuators with friction; DAMADICS Benchmark; Fault detection; Neural networks

Indexed keywords

BENCHMARKING; FINITE DIFFERENCE METHOD; FRICTION; MECHANICAL ACTUATORS; NEURAL NETWORKS; PLANT MANAGEMENT; SUGAR FACTORIES;

ACTUATOR FAULT DETECTION; APPROXIMATION SCHEME; DAMADICS BENCHMARK PROBLEMS; FRICTION MODELING; INDUSTRIAL PROCESSS; MECHANICAL FRICTION; MECHANICAL SYSTEMS; NOVEL METHODOLOGY;

FAULT DETECTION;

EID: 85064447499     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/S1474-6670(17)36617-X     Document Type: Conference Paper

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