FEA (Finite Element Analysis) of conventional hysteresis motors has been difficult due to the specificity of the rotor. Equivalent circuit-based analysis is common, and it is difficult to expect the torque ripple due ...
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In general, to improve the output performance of motors, multi-level inverter that can create a waveform close to a sinusoidal wave by applying a voltage with a waveform that has minimal harmonic components are propos...
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In motor fault diagnosis, auto-encoder based methods are effective in detecting abnormal patterns by utilizing only normal data. However, this approach has limitations in classifying various fault types, as it is prim...
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At present, in the fields of mobile communication, aerospace and et al., the signal transmission quality problems caused by the structural damage of the coaxial cable have become increasingly prominent, and the fault ...
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Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision-making in highways is essential. However, decision-making in highways is a challenging task...
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Lithium-ion batteries are prone to capacity degradation during use, leading to a decline in their reliability and potentially causing catastrophic failures. This necessitates the study of advanced methods for estimati...
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This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural heads...
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Radio electronic devices operating in differential mode, such as those used in automatic controlsystems, can be adversely affected by electromagnetic interference. This is particularly relevant for interfaces like US...
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This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, w...
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This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, which can compute the bounds of the output of a feedforward neural network subject to a bounded input. By applying the proposed interval analysis method to a network trained with fault-free system data, adaptive thresholds for fault detection are computed. Finally, one can acquire fault detection results via a fault detection strategy. The proposed method can achieve tight bounds of the network output and employ simple operations, which leads to accurate fault detection results and a low computational burden.A numerical simulation and an experiment on an AC servo motor are given to illustrate the effectiveness and superiority of the proposed method.
The widespread of Electric vehicle (EV) is increasing fastly because of many reasons like environment safety issue, best performance and its suitable cost for customers and due to these reasons researchers give an imp...
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