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A high degree of automation in flexible production units require powerful tools for supervision and fault detection to maintain quality and productivity. In this paper, an observer-based fault detection method is proposed which makes use of non-measurable process information instead of installing as many sensors as possible. The detection method is reviewed and applied to the fault detection problem in an industrial robot, using a dynamic robot model. The robot model is enhanced by the inclusion of nonlinear friction terms. A new residual evaluation approach of model-based fault detection methods is investigated for processes which exhibit unstructured disturbances, arising from model simplification. The present analytical approaches are applicable only to structured approaches. In this paper a fuzzy-logic approach is presented which is capable to address unstructured disturbances as well. Finally, some practical results for an industrial robot example are presented.
Sneider et al. (Wed,) studied this question.