## 5 The Health Assessment Program Methodology  
Based on the results of the previous sections, we propose a methodology for diagnosing the health status of industrial robot joints with torque signature analysis. The diagram representing the methodology can be seen in Figure 12. The main idea behind the proposed methodology is that a joint that suffers a mechanical degradation will require higher torque to execute a certain trajectory than a healthy one. As the time goes by, the mechanical elements attached to the motor (i.e., The brake and the reducer) will inevitably suffer mechanical deterioration. This will require higher effort to execute the same trajectory. To illustrate the methodology, let's say that a robot \(R_{1}\) executes a certain program \(P_{1}\) and needs to apply torque \(T_{1}\) in a joint to complete the trajectory. If there is any mechanical deterioration, the system will be less efficient, but the robot controller will make sure that this deterioration does not affect the accuracy of the robot. Even if the accuracy remains invariant, to finish the same program \(P_{1}\) the required torque now (\(T_{2}\)) will be higher than before (\(T_{2}>T_{1}\)).  
We therefore propose to use a specific trajectory-tool combination in the robots of the production line to assess their current health status. As described in the diagram of Figure 12 robots will execute a predefined non-trivial trajectory with a known load and they will require a certain amount of torque in each joint to complete this trajectory. To acquire torque data in all the joints, the trajectory must use the whole set of joints comprising the robot manipulator. We will call _Health Assessment Program_ (_HAP_) to that predefined trajectory-load combination. These two specifications will always have to  
Figure 11: Mean standby orientation no recorded failures (0) and with failures (1).  
Figure 12: Diagram of the proposed methodology. First, the trajectory and tool are defined. Then, the program is executed and torque data is acquired. The recorded data is stored in a server as a reference along with the robot’s ID. The process is periodically repeated and the new signals are compared with previously recorded ones to diagnose a possible deterioration.  
remain unchanged in order to make a fair comparison of the results. However, an advantage of the methodology is that the precise shape, center of mass and inertia of the load are not required to be known or modeled.  
The first step is to execute the trajectory with the robot attaching the corresponding load. The torque of each joint will be recorded during the the whole process, producing a digital _signature_ of the torque of each joint. This initial torque data or _signature_ will be used as a reference for that particular robot. This initial data will be stored with an identification number of the robot. Whenever we want to analyse the mechanical deterioration of the joints of that robot, we will run the _HAP_ again and compare the previously stored values with the recently acquired ones. If there is no change in the torque values, we can conclude that there is no significant mechanical deterioration in the joints yet. In contrast, if there is some increase in the torque of a certain joint, it will mean that the motor of that joint is requiring more effort than expected.  
The proposed methodology is applicable to any joint or industrial robot. In addition programming the _HAP_ as an additional trajectory to the usual routine of the robots is enough. It is not necessary to take the robot off the production line to diagnose it. Which is a significant advantage compared to existing condition monitoring techniques.