Eddy current brakes have been recently used for functional resistance training in individuals with neurological and orthopaedic disorders. These devices consist of a gearbox, a conductive disc, and permanent magnets that can be moved relative to the disc to alter resistance. However, current devices use a commercial planetary gearbox with a tall profile that sticks out from the leg, which affects wearability. This is coupled with the large system inertia, which together impedes potential device transition to clinical and in-home use. In this study, we developed a low-profile, pancake-style planetary gearbox that greatly reduces the protrusion of the device from the leg. We performed a design analysis and optimization to minimize the thickness and inertia of the device while ensuring that it could withstand the maximum expected torque (50 Nm). We then performed human subjects experiments to examine the effectiveness of our new design for functional resistance training. The results indicated that all leg muscles showed a significant increase in activation during resisted conditions. There were also significant after-effects on medial hamstring activation. These results indicate that the new design is a feasible method for functional resistance training and may have a potential clinical value in gait rehabilitation.
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http://dx.doi.org/10.1109/TBME.2024.3444688 | DOI Listing |
Sensors (Basel)
November 2024
Institute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin 150080, China.
The working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the interrelationships between data points. To address these challenges, we proposed a fault diagnosis method for planetary gearboxes that integrates Markov transition fields (MTFs) and a residual attention mechanism.
View Article and Find Full Text PDFSensors (Basel)
October 2024
China North Vehicle Research Institute, Beijing 100072, China.
Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions.
View Article and Find Full Text PDFSensors (Basel)
August 2024
School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
ISA Trans
November 2024
Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China. Electronic address:
Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM).
View Article and Find Full Text PDFFront Robot AI
August 2024
Institute for Machine Elements, Gear Research Center (FZG), Technical University of Munich, Munich, Germany.
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