A Preliminary Experimental Study on the Effects of Wear on the Torsional Stiffness of Strain Wave Gears

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

Opinion polarisation in social networks

In this paper, we propose a Boltzmann-type kinetic description of opinion formation on social networks, which takes into account a general connectivity distribution of the individuals. We consider opinion exchange processes inspired by the Sznajd model and related simplifications but we do not assume that individuals interact on a regular lattice. Instead, we describe the structure of the social network statistically, assuming that the number of contacts of a given individual determines the probability that their opinion reaches and influences the opinion of another individual. From the kinetic description of the system, we study the evolution of the mean opinion, whence we find precise analytical conditions under which a polarization switch of the opinions, i.e. a change of sign between the initial and the asymptotic mean opinions, occurs. In particular, we show that a non-zero correlation between the initial opinions and the connectivity of the individuals is necessary to observe polarization switch. Finally, we validate our analytical results through Monte Carlo simulations of the stochastic opinion exchange processes on the social network

Intelligent Diagnostics for Aircraft Hydraulic Equipment

In aviation industry, unscheduled maintenance costs may vary in a large range depending on several factors, such as specific aircraft system, operational environment, aircraft usage and maintenance policy. These costs will become more noteworthy in the next decade, due to the positive growing of worldwide fleet and the introduction of more technologically advanced aircraft. New implemented technologies will bring new challenges in the Maintenance, Repair and Overhaul (MRO) companies, both because of the rising number of new technologies and high volume of well-established devices, such as Electro-Hydraulic Servo Actuators for primary flight control. Failures in aircraft hydraulic systems deeply influence the overall failure rate and so the relative maintenance costs. For this reason, overhaul procedures for these components still represents a profitable market share for all MRO stakeholders. Innovative solutions able to facilitate maintenance operations can lead to large cost savings. This paper proposes new methodologies and features of the Intelligent Diagnostic system which is being developed in partnership with Lufthansa Technik (LHT). The implementation of this innovative procedure is built on a set of failure detection algorithms, based on Machine Learning techniques. This development requires first to bring together the results from different parallel research activities: 1. Identification of critical components from historical data; 2. Designing and testing automatic and adaptable procedure for first faults detection; 3. High-fidelity mathematical modeling of considered test units, for deeper physics analysis of possible failures; 4. Implementation of Machine Learning reasoner, able to process experimental and simulated data

Collaborative Robotics: Enhance Maintenance Procedures on Primary Flight Control Servo-Actuators

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

A Model-based Framework for Industrial and Collaborative Robots Health-management

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Preliminary Experimental Study on the Effects of Wear on the Torsional Stiffness of Strain Wave Gears

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

Network-based kinetic models: Emergence of a statistical description of the graph topology

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interactions a statistical description of the graph topology, given in terms of the degree distribution embedded in a Boltzmann-type kinetic equation, is sufficient to capture the collective trends of networked interacting systems. This proves the validity of a commonly accepted heuristic assumption in statistically structured graph models, namely that the so-called connectivity of the agents is the only relevant parameter to be retained in a statistical description of the graph topology. Then, we validate our results by testing them numerically against real social network data