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.

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.