Industrial robots have been used as a key factor to improve productivity, quality and safety in manufacturing. Many tasks can be done by industrial robots and they usually play an important role in the system they are used, a robot stop or malfunction can compromise the whole plant as well as cause personal damages. The reliability of the system is therefore very important.
Nevertheless, the tools available for maintenance of industrial robots are usually based on periodical inspection or a life time table, and do not consider the robot’s actual conditions. The use of condition monitoring and fault detection would then improve diagnosis.
The main objective of this thesis is to define a parameter based diagnosis method for industrial robots. In the approach presented here, the friction phenomena is monitored and used to estimate relevant parameters that relate faults in the system. To achieve the task, the work first presents robot and friction models suitable to use in the diagnosis. The models are then identified with several different identification methods, considering the most suitable for the application sought.
In order to gather knowledge about how disturbances and faults affect the friction phenomena, several experiments have been done revealing the main influences and their behavior. Finally, considering the effects caused by faults and disturbances, the models and estimation methods proposed, a fault detection scheme is built in order to detect three kind of behavioral modes of a robot (normal operation, increased friction and high increased friction), which is validated within some real scenarios.
2007. , 79 p.