## 2 Related Work  
When talking about Condition Monitoring, Fault Detection or Predictive Maintenance in Industrial Robots, more specifically in manipulators, we face the problem of choosing the best sensor and technique to detect and if possible prediction failures may lead to production stops with relative downsides. As discussed during the Introduction, there are two crucial aspects to take into account: (1) there are several mechanical components in a single system and considering their effects together increases the difficulty in analysis; and (2) the same system can be used for many different tasks, making it hard to define a method that may work under every possible working condition. Regarding the first aspect, the mechanical components most affected by failures are the robot joints [4; 5; 6]. Problems are generally related to the electric motor or to the speed reducer and specifically to some particular part of these two components (i.e., motor windings, bearings, shaft, breaks, gears of the reducer, etc.). Sensor data that mainly have provided the best results in this context are motor current, motor torque, or vibrations [7], also used sometimes in combination with other system states. For instance, [PERSON] et al. developed a detection method for the bolt loosening of industrial robot joints based on electromechanical modeling and motor current signature analysis (MCSA) [8]. [PERSON] et al. in [9] proposed prognostic health management for the robotic rotate vector reducer using external sensors mounted on the robot and managed using an embedded system to electrical MCSA for mechanical fault detection. [PERSON] et al. proposed a new method to evaluate the health indicators for industrial robots and suggest a new health indicator (HI) based on vibration data measurements [10]. In Ref. [11], an Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators is proposed using different information (i.e., joints current, torque, speed, etc.). In particular, they compare 15 methods for anomaly detection, including methods based on principle component analysis, local outlier factor, and autoencoders assessed in a typical pick-and-place application with respect to their capacity to detect a broad range of exogenous anomalies. [PERSON] et al. proposed a non-intrusive methodology for industrial robot joint health assessment using torque sensor data by creating a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration [12]. In Ref. [13], the authors proposed a Gaussian mixture model-based unsupervised fault detection framework to detect the faults in industrial robots using current signals. In Ref. [14], they used torque estimation for gearbox failure in industrial robots proposing an improvement of detection performance with respect to machine learning approaches using a technique to curate the data prior to building a classification model. While in [15], the authors proposed a design method of trajectories for detecting the excessive clearances of passive joints in a DELTA robot on the basis of the phenomenon that the actuation torque waveform fluctuates due to the collision between joint elements. Based on these previous work considerations, our approach will be based on Motor Torque Analysis (MTA), since in the authors' experience, torque analysis also provided interesting results regarding the diagnosis under non-stationary conditions (i.e., varying speed and load conditions) [16]. In fact, in [17], we already pointed out the possibility of overcoming limits related to bearing fault diagnosis under non-stationary conditions using MTA instead of MCSA. Furthermore, in [18], a review regarding the detection and diagnosis methodologies for gearboxes, it is demonstrated that the most useful signals for diagnosis are currents, torques, or vibrations.