Fault detection in mechanical systems with friction phenomena: an online neural approximation approach.
In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity. The friction internal state is not assumed to be available for measurement. The neural fault detection methodology is analyzed with respect to its robustness and sensitivity properties. Rigorous fault detectability conditions and upper bounds for the detection time are also derived. Extensive simulation results showing the effectiveness of the proposed methodology are provided, including a real case study on an industrial actuator.
Tissue Engineering, Department Forschung Lab 422, Universitätskliniken, Basel 4031, Switzerland.
Adam
A
Papadimitropoulos
George A
GA
Rovithakis
Thomas
T
Parisini
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D004868:Equipment Failure; D019544:Equipment Failure Analysis / Q000379:methods*; D017276:Friction; D019563:Mechanics; D008962:Models, Theoretical; D016571:Neural Networks, Computer