## 1 Introduction  
In recent decades, research in industrial robots focused mainly on improving manufacturing processes, optimizing trajectories, improving accuracy, etc. However, predictive maintenance and health assessment of robots, has not received as much attention [1; 2]. From a maintenance point of view, industrial robots are a complex kinematic chain comprised of several mechanical components that have been extensively studied individually: Motors, speed reducers, gears and bearings just to cite some. Notwithstanding, the union of all these components in a single system and its maintenance as a whole, significantly increases the complexity of failure prediction. In general, health assessment techniques for machinery can be classified in two main groups: physical model-based and data-driven [3]. The former uses deterministic mathematical models to describe the expected behavior of a given system and compares this expected behavior with the real behavior. The latter, analyses data captured with sensors and applies statistical and machine learning methods to detect patterns and predict behavior. Model-based approaches have been widely used to detect failures in industrial components [4; 5; 6]. Unfortunately, it is often difficult to implement an analytical model that accurately describes the behavior of such complex systems. In order to build an analytical model of an industrial robot, there are some approximations and assumptions that have to be made such as constant speeds, temperature of the lubricant, loads, etc. [7]. These necessary approximations distance the model from the real behavior of the system, thus data-driven approaches can be more accurate for industrial robot health assessment [1]. In addition, the expansion of the Industrial Internet of Things (IIoT) and Big Data technology in the era of smart manufacturing [8] is pushing the way towards the implementation of reliable data analysis solutions for predictive maintenance. Data-based monitoring and IoT solutions are rapidly emerging and transforming the manufacturing industry into an industrial Big Data environment [9]. Several research groups have addressed the issue of monitoring the mechanical condition of machine-tools and industrial robots with data-driven approaches [10; 11; 12; 13]. [PERSON] et al. [14] developed a holistic framework for milling and CNC machine tool modelling using the OPC-UA communication standard. They implemented a data acquisition device in order to integrate machine-tools without connectivity in their solution. [PERSON] et al. [15] proposed a multi-sensor system for machine tool axes monitoring and degradation assessment. [PERSON] [2] developed an embedded system for industrial robot condition monitoring using accelerometers at the flange of the robot. He detected a mechanical failure in the gears of the robot joints and emphasized the need for more research in the field of robot predictive maintenance. The drawback of using this approach for robots in a real assembly line is that it is hard to isolate external vibrations from vibration caused by the robot's failure. To overcome this issue, acoustic emission sensors were used in [16] to detect a faulty rolling bearing on a welding robot joint. An important issue to take into account with both accelerometers and acoustic emission sensors, is that they are intrusive in the sense that they have to be attached to the structure of the robot. This can be a drawback in real assembly lines because a sudden detachment of one of these sensors could cause either a stop in the production line, or a defect in manufactured products. Lubricant or wear debris analysis is also commonly used for industrial robot joint health assessment. It consist on analysing the wear particles inside the oil that lubricates the joints. As illustrated in [17], any mechanical element working in contact with another mechanical element will deteriorate and degrade regardless of the design.