Industrial robots play a key role in production lines. As a direct consequence, even a slight degradation of their operating conditions could negatively affect the entire manufacturing process. To minimize economical losses, preventive measures such as planned maintenance or stand-by working stations are adopted and, in some cases, additional manipulators are installed to ensure line availability. However, since each robot performs a specific task, it degrades at a different rate from another one, so Planned Preventive Maintenance (PPM) should be replaced with Condition Based Maintenance (CBM) for a more efficient and cost-effective approach. In addition, collaborative robots (cobots) have become more and more popular in the past years increasing the level of automation, in particular in small and medium size companies. Therefore, a failure of an industrial manipulator could not only cause unexpected downtimes, but also jeopardize the safety of the personnel with whom it shares its workspace. A cobot, in fact, is labeled as safe as long as it works in nominal conditions, but this cannot be guaranteed otherwise. Within this framework, Prognostics and Health Management (PHM) techniques could be used for both a customized maintenance on a specific machine and for preventing undesired events like the aforementioned ones. An ongoing research activity at Politecnico di Torino is focused on a model-based approach able to better describe the behavior of an industrial robot in non-nominal operating conditions. In order to properly simulate faults and failures of a manipulator, it has been first necessary to analyze its failure modes and their impact on the entire system. A robot, in fact, is usually equipped with internal sensors and algorithms able to monitor its health status. However, this is only true for software or electrical failures (i.e. power supply, motor or sensors related), but it is not as effective in case of mechanical ones like bearings or gearbox wear. This paper firstly presents an overview of the possible failures related to harmonic drives which are often used in robotics due to their compact and light-weight design and the high reduction ratio. Then, it outlines the work done with the objective of providing an accurate, physics-based description of faults progression of harmonic drives used in industrial robots and the related high-fidelity models in the framework of a PHM system for fault and failure detection, isolation and remaining useful life (RUL) prediction.

Dynamic parameters are crucial for the definition of high-fidelity models of industrial manipulators. However, since they are often partially unknown, a mathematical model able to identify them is discussed and validated with the UR3 and the UR5 collaborative robots from Universal Robots. According to the acquired experimental data, this procedure allows for reducing the error on the estimated joint torques of about 90% with respect to the one obtained using only the information provided by the manufacturer. The present research also highlights how changes in the robot operating conditions affect its dynamic behavior. In particular, the identification process has been applied to a data set obtained commanding the same trajectory multiple times to both robots under rising joints temperatures. Average reductions of the viscous friction coefficients of about 20% and 17% for the UR3 and the UR5 robots, respectively, have been observed. Moreover, it is shown how the manipulator mounting configuration affects the number of the base dynamic parameters necessary to properly estimate the robots’ joints torques. The ability of the proposed model to take into account different mounting configurations is then verified by performing the identification procedure on a data set generated through a digital twin of a UR5 robot mounted on the ceiling.

Digital models of industrial and collaborative manipulators are widely used for several applications, such as power-efficient trajectory definition, human–robot cooperation safety improvement, and prognostics and health management (PHM) algorithm development. Currently, models with simplified joints present in the literature have been used to evaluate robot macroscopic behavior. However, they are not suitable for the in-depth analyses required by those activities, such as PHM, which demand a punctual description of each subcomponent. This paper aims to fill this gap by presenting a high-fidelity multibody model of a UR5 collaborative robot, containing an accurate description of its full dynamics, electric motors, and gearboxes. Harmonic reducers were described through a translational equivalent lumped parameter model, allowing each constitutive element of the reducer to have its decoupled dynamics and mating forces through non-linear penalty contact models. To conclude, both the mathematical model and the real robot on a test rig were tested with a set of different trajectories. The experimental results highlight the ability of the proposed model to accurately replicate joint angular rotation, speed and torques in a wide range of operational scenarios. This research provides the basis for the development of a model-based PHM-oriented framework to carry out detailed and advanced analyses on the effects of manipulator degradations.

Strain wave gears, also known as harmonic drives, are employed in a wide range of fields such as robotics and aerospace, where light weights, precision, and reliability are essential to the correct execution of the tasks. For this reason, their understanding and optimization are of high interest for both academia and industry. Previous studies have been mainly focused on investigating and modeling the working principle of strain wave gears in nominal operating conditions. On the contrary, the present paper describes the results of an experimental campaign aimed to introduce wear in gears of two different suppliers and its impact on the gear torsional stiffness. Results show how the change in the gear performance strongly depends both on the gear manufacturer and the location of wear. For the analyzed components, a damaged wave generator–flexspline interface reduces the gear stiffness up to one-fourth of its nominal value, while the non-nominal shape of the teeth jeopardizes the gearbox performance, leading up to just 4% of the nominal stiffness values, and resulting in backlash. Such data can be used to properly model the presence of wear in strain wave gears and to train data-driven diagnostics and prognostics routines to effectively detect such a fault.

Electro-Hydraulic Servo-Actuators (EHSAs) are mainly used to command primary flight control surfaces in military and commercial aircraft. Since these devices are crucial for vehicle stability and maneuverability, a correct assessment of their health status is mandatory. Within this framework, a joint research project (HyDiag), held by Politecnico di Torino and Lufthansa Technik AG (LHT), aims to provide a more efficient and reliable procedure to determine the operating conditions of the EHSA. A smart and automatic sequence, able to extract several health features of the Unit Under Test (UUT), has been developed and integrated. The present paper discusses the implementation of a collaborative robot, equipped with a vision system and customized tools, for both health features extraction, and maintenance tasks on unserviceable servo-actuators. The main challenges related to the automation of such complex tasks in a real working environment are highlighted, togetherwith the advantages brought by the proposed approach. The paper also presents the first results of an ongoing experimental campaign. Specifically, it reports the enhancements of the maintenance procedures using collaborative robotics and possible future developments.