Neural network approach to condition monitoring of industrial robotic-manipulators

The paper presents a novel technique for condition monitoring of industrial robotic manipulators, which is based on neural network analyses of the dynamic model parameters obtained by means of on-line identification. There were derived analytical expressions that allow minimising impact of the measurement errors on the identification accuracy. Efficiency of the proposed technique has been verified by real-life case studies from industrial monitoring systems.