electric motor
The dataset used in this paper contains a total of two , one is the bearing dataset from the University of Paderborn, which uses a total of five KA01,KA04,KA05,K003,KI01 and the author's laboratory self-built dataset, which is divided into a total of six, normal signals, turn-to-turn short circuits, rotor misalignments, rotor breaks, in-bearing damages, and out-of-bearing damages, and both of them use vibration signals as well as current signals were used to generate training as well as experiments.
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A video presentation on lod loader working as it is driven by energy converter designed for smooth control and energy recuperation.
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This dataset includes the rotor geometrical parameters (*.csv) and motor parameters (*.csv) of interior permanent magnet synchronous motors. The rotor geometry covers three structures: 2D-, V-, and Nabla-structures. The motor parameters are generated by machine learning based on the finite element analysis results. The software JMAG Designer 19.1 was used for the finite element analysis.
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This dataset contains two subjects. The first one is a Matlab App created for PMSMs (Permanent Magnet Synchronous Motors) parameters estimation, both in the electrical and in the mechanical energetic domains. The second one is a generalized PMSM Simulink superblock equipped with a user-friendly interface allowing to select options and to input the model parameters. The Matlab App and the PMSM superblock can be easily interfaced with each other.
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878 Views
This dataset includes the rotor geometry image (*.png) and motor parameters (*.csv) of interior permanent magnet synchronous motors. The rotor geometry covers three structures: 2D-, V-, and Nabla-structures. The motor parameters are generated by machine learning based on the finite element analysis results. The software JMAG Designer 19.1 was used for the finite element analysis.
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2227 Views