Model-Based Fault Diagnosis of Industrial Robots: A Comparison of Different Methods

Abstract In modern manufacturing systems it is very important to increase the safety, reliability and availability of the involved components, e.g. industrial robots. Downtime of the entire production line as a result of a single component breakdown will cause high costs which have to be avoided. Hence automatic controlled systems of high complexity should be supervised by automatic means and supplemented by appropriate diagnosis methods. Continuous time model-based procedures have been proven to present powerful tools for this purpose. In the present contribution specific approaches of parameter estimation and state estimation are applied to the mechanical components of an industrial robot. Several changes which represent typical mechanical faults were established artificially to the robot. Finally the different approaches are compared.