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4 articles for “FEA Magnetic Flux”
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Detection of Air Gap Eccentricity Faults Using Finite Element Analysis (FEA) in BLDC Motors
Abstract: Abstract: - Detection of eccentricity faults has been feasible using physical and vibrational parameters. It is essential to detect and diagnose such faults at the early stage through measurement and monitoring of changes in the magnetic flux of the BLDC motor in the motor windings and stator slots. In this paper, brushless dc motor and its stator, rotor and windings are designed using FEA method .Changes in magnetic flux of …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 2, 2018 · pp. 1–5 Read article
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Detection of Stator Winding Faults Using Finite Element Analysis (FEA) in BLDC Motors
Abstract: Detection of eccentricity faults has been feasible using physical and vibrational parameters. The Brushless DC motor is undergoes various kinds of stresses including mechanical, electrical, thermal, and environmental. These stresses increase the probability of occurrences of stator faults with various severity levels. The well maintained BLDC motor and correct operations decrease the probability of occurrences of faults. These faults are of three types as damage in laminations, damage in frame …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 3, 2018 · pp. 82–90 Read article
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Design And Fabrication of Contactless Magnetic Gears
Abstract: Modern engineering prioritizes speed, efficiency, and reliability, spurring innovations like contactless magnetic gear systems that transmit torque through interacting magnetic fields from permanent magnets, eliminating physical contact to drastically cut friction, backlash, wear, lubrication needs, and heat—operating silently with minimal vibration and peak efficiencies up to 99% under ideal conditions, outperforming traditional gears in low-speed, high-torque direct-drive applications such as motors. The design centers on two rotors: an outer high-speed …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 42–49 Read article
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Motors Using a Deep Learning-Based Torque Control with Torque Ripple Reduction under Nonlinear Magnetic Conditions
Abstract: This research discusses a deep learning strategy for torque management to minimize the effect of torque ripple in a nonlinear electric motor. Nonlinear electric motor losses may include: magnetic saturation, harmonic flux losses and inverter losses. In many cases when the system parameters deviate and/or instability issues occur, the traditional method with a model-based approach or PI control may encounter challenges. In this case, the authors proposed a hybrid approach …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article