## 2 Experimental Design and Implementation  
We designed an experiment to compare the torque applied by a faulty robot joint and a healthy one. If the mechanical wear has a significant effect in the effort of the joint, our hypothesis is that the faulty joint would require higher torque than a healthy joint to execute a given trajectory. Therefore, the methods selected to identify a faulty joint should focus on measuring the increment of the torque applied in the robot joints, whether they are statistical methods or machine learning models. Figure 1 describes the process of the experimental procedure carried out. First, a faulty robot wrist was removed from an automotive assembly line after years of uninterrupted work. The faulty robot caused a sudden stop in the production line and it was replaced by a new one. The experiment was performed using a 6 DOF industrial robot (ABB IRB 6400r), two robot wrists (the faulty wrist and the new one), two loads representing the 15% and 90% of the maximum payload of the robot and four torque sensors, two sensors for each wrist, located in the 5 th and 6 th joints. These sensors are factory built-in torque sensors and the robotic systems uses them in the control feedback-loop. The ABB IRB 6400r is a widely used industrial robot in the automotive industry with a maximum payload of 200 Kg. A non-trivial fixed trajectory was programmed in order to excite the robot joints. First of all, the faulty wrist was installed in the ABB robot in a laboratory facility, out of the production line. Then, the programmed trajectory was executed three times with three different loads each time. The loads represented the 0%, 15% and 90% of the maximum payload of the robot. We executed the trajectories and collected the torque data with a sampling rate of 100 ms. Afterwards, the faulty wrist  
Figure 1: Flowchart of the experimental procedure carried out. was removed and the new one installed in the same ABB robot. The trajectory was repeated again three times with the same three different loads in each repetition. Therefore we collected the data of the torque applied in the 5 th and 6 th joints throughout the six trajectory executions (three with the faulty wrist and tree with the new wrist). After completing all these trajectories, torque signals were acquired and stored in csv files following the format specified in Table 1. The signals were stored as floating point numbers and using the standard unit (Nm) for the torque. We used the TCP/IP communication protocol to connect with the robot controller and capture the torque signals, which is an Industry 4.0 communication standard. The TCP/IP protocol is suitable for Industry 4.0 and Big Data scenarios, as it is able to interconnect large number of devices [19]. Some researchers have used the OPC-UA communication protocol [9] which is built on top of TCP/IP for data acquisition in industrial scenarios. The data shown in Figures 2 and 3 disclose an evident increment in the torque of the faulty wrist's 5 th joint. This increment is clearly appreciable in all the three experiments and throughout the execution of the whole trajectory, therefore the effort required by the motor of the 5 th joint was higher than expected with any of the three loads and in any robot pose or movement. In contrast, the torque of the 6 th joint does not change significantly in any experiment. The increment in the torque is measured by first calculating the absolute value of the acquired signals. The absolute value of the torque in each joint is then integrated to calculate the total amount of torque applied throughout the whole trajectory in all the experiments. Once the total applied torque is calculated, the percentage of increase between the two wrists is calculated. Tables 2 and 3 show the results of the 5 th and 6 th joints respectively.