## 6 Discussion and Conclusions  
We reported a methodology for industrial robot health assessment. The methodology was validated experimentally comparing the torque of two robot wrists. These results show that torque sensors provide reliable information to detect mechanical deterioration of an industrial robot's joint. We carried out the comparisons with different loads and the increment in the torque was clearly appreciable with the three tested loads. Therefore, the methodology is proved to be useful with any load configuration. The recorded torque data shows an homogeneous increase in the faulty wrist. The source of the malfunction was located in the permanent magnets of the motor brakes with an in depth mechanical inspection. We measured the magnetic field of the permanent magnets and the hysteresis curves showed a 24% of magnetic field loss in the permanent magnets of the faulty joint. The effect of this magnetic field loss can be effectively detected with the proposed methodology. A direct conclusion of the work is that the electrical consumption of the faulty joint was approximately twice that of a healthy joint. Therefore, even if a robot does not show any apparent malfunction it might still be working in conditions which are far from ideal due to mechanical deterioration and fatigue. The proposed method could help manufacturers to monitor not only the mechanical condition of the robot joints but also the electrical over-consumption i.e., Detecting the most energy-consuming robots or work cells. Controlling the energy consumption of robots is a fundamental factor to achieve sustainable factories and to reduce pollution. Another significant advantage of the methodology is that torque measurement is done inside the robot's physical structure. As we mentioned in the introduction, if a sensor or a wire installed in the outside part of the robot's structure detaches and falls into the production line, it could cause significant damage to the product being manufactured or even stop the production line. Therefore, since torque sensors are inside the robot's physical structure, this possible inconvenience is dismissed. Last but not least, the methodology is applicable to any industrial robot, as long as the acquired data is compared with robots of the same model. Hence, our approach does not depend on the robot manufacturer or the robot type. It only depends on the programmed trajectory and the carried load, which are both configurable by researchers and practitioners. A limitation of the presented work is that the detection of torque deviation depends on a pre-defined tool and trajectory configuration. Therefore, if either of these two characteristics change, torque signals would also inevitably change and the data regarding the monitored robot should be readjusted. Another limitation of the presented work is that the execution of the _Health Assessment Program_ requires the robot to momentarily stop its normal production behavior to execute a pre-defined trajectory and acquire the correct torque signals. An identified future line of work is to implement machine-learning models to detect anomalies in the torque of robot joints with different tool and trajectory configurations. Although our approach requires a specific trajectory and tool configuration for the data to be comparable, a machine-learning model might be flexible enough to detect deviations with different trajectory and tool configurations and extrapolate the behavior learned in one use case to the rest. Another interesting future line of work would be to use the proposed methodology to train predictive models to estimate the RUL of industrial robots in real production line conditions. Our methodology effectively detects deviations from the normal behavior of robot joints in real world scenarios, but further research is needed to accurately assess the RUL of the monitored robotic systems. Finally, torque data monitoring could also be used to find an optimal standby pose of industrial robots in order to minimize the effort of the joints.