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A novel radial runout measurement method for gear motors using a microsensor based on all-fiber Fabry-Perot interferometry is investigated. In order to achieve the fault diagnosis, in this method, a single-mode fiber is put forward as a sensor to measure radial runout of the rotating shaft. The performance of the proposed sensor has been compared to a Portable Digital Vibrometer-100 laser vibrometer for validation purposes, and the results show that the difference between them is approximately ±0.55µ.
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http://dx.doi.org/10.1364/AO.412357 | DOI Listing |
PLoS One
September 2025
Scientific and Research Centre for Fire Protection, National Research Institute, Józefów, Poland.
The general theory of oil pumping using gear pumps shows that as kinematic viscosity increases, so does the energy requirement to drive the pump shaft. However, modern oils used in machines and vehicles are characterized by a wide range of modifiers that alter their physical and chemical properties. This article presents a study on the energy demand for driving a gear pump while pumping commercial oils used in machines and vehicles (16 types), such as those for combustion engines in single-drive and hybrid vehicles, gearboxes, hydraulic systems, shock absorbers, chainsaw lubrication, and two-stroke engine fuel mixtures.
View Article and Find Full Text PDFJ Med Eng Technol
August 2025
Orthotics and Prosthetics Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
The growing need for efficient patient lifting and transfer solutions highlights a significant gap in current healthcare systems, particularly in affordable, accessible options for home use. While most research has focused on automated or motorised systems, this study introduces a novel manual patient lifting device based on a worm gear mechanism, which, despite its proven industrial benefits, remains underexplored in healthcare. Using a case study of a 50-year-old, 72 kg individual, we developed a cost-effective, manually operated lifting system aimed at reducing caregiver workload and improving patient mobility.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Basic Sciences, Higher Technological Institute, 10th of Ramadan City, Egypt.
In the field of brain‒computer interfaces (BCIs), developing a reliable machine learning (ML) model for real-time robotic hand control systems based on motor imagery (MI) brain signals requires substantial research. For this purpose, a set of ML models has been developed and tested to identify robust models via MI sensor data fusion under both nonadversarial and adversarial attack conditions. This paper addresses numerous essential areas, including the development of ML models for electroencephalography (EEG) MI signal datasets, with a focus on proper preprocessing and evaluation under both nonadversarial and adversarial attack conditions.
View Article and Find Full Text PDFJ Opt Soc Am A Opt Image Sci Vis
February 2025
We presented a portable lensfree imaging platform that can achieve complex wavefield reconstruction with multi-distance intensity measurements. In our platform, 400 LEGO bricks and a bare CMOS sensor chip are integrated into a lensfree in-line holographic imaging system, where a motor and a set of gear modules from LEGO are designed to control the axial movement of a sample to generate multi-plane intensity patterns. In data processing, the intensity images are calculated in a computer to show the retrieved amplitude and phase of the sample.
View Article and Find Full Text PDFTribol Trans
May 2025
Imperial College London, London, UK.
This paper uses a newly developed tribology-based system-level transmission efficiency model to investigate the influence of e-fluid properties on electric vehicle (EV) drivetrain losses. The model considers gear meshing losses using a thermally-coupled mixed friction prediction, bearing losses using existing models, and gear churning using a new experimentally-derived regression equation. The key advantages of the approach are: (i) it is a system-level approach that accounts for the interdependency of different sources of losses by predicting the evolution of temperature distribution in the entire electric drive unit (EDU) including the transmission, e-motor and heat exchanger; (ii) it can discriminate between two oils of the same specification in terms of their impact on overall losses by using measured lubricant rheology; and (iii) it predicts total energy loss over any vehicle duty cycle.
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