Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, an...
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PurposeThe purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).Design/m...
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PurposeThe purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).Design/methodology/approachIn this paper, the ampere-conductor wave model of HVLSPMSM after ITSF is established. Second, a mathematical model of the magnetic field after ITSF is established, and the influence law of the ITSF on the air-gap magnetic field is analyzed. Further, the mathematical expression of the electromagnetic force density is established based on the Maxwell tensor method. The impact of HVLSPMSM torque ripple frequency, radial electromagnetic force spatial-temporal distribution and rotor unbalanced magnetic tension force by ITSF is revealed. Finally, the electromagnetic-mechanical coupling model of HVLSPMSM is established, and the vibration spectra of the motor with different degrees of ITSF are solved by numerical *** this study, it is found that the 2np order flux density harmonics and (2 N + 1) p order electromagnetic forces are not generated when ITSF occurs in ***/valueBy analyzing the multi-harmonics of HVLSPMSM after ITSF, this paper provides a reliable method for troubleshooting from the perspective of vibration and torque fluctuation and rotor unbalanced electromagnetic force.
Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, an...
Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, and records within the same attribute column often exhibit high similarity, providing favorable conditions for data compression. Researchers have proposed numerous compression algorithms, but these algorithms can only achieve optimal compression results when applied to data with specific features. Therefore, it is crucial to select the compression algorithm that performs best based on the features of the data. We propose a novel method for selecting compression algorithms using Stacking, a technique that effectively enhances the prediction ability of classification models in machine learning. Our experimental results demonstrate that the stacked model achieves an accuracy of 92.9%, surpassing other models in terms of F1-Score and Kappa coefficient. Furthermore, our compression algorithm selection method outperforms other existing methods in terms of compression performance.
This paper focuses on kinematic calibration of a novel 5-DOF hybrid robot and attention is paid to the suppression of 'observation noise' impact caused by the measurement system and unconsidered source errors....
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This paper focuses on kinematic calibration of a novel 5-DOF hybrid robot and attention is paid to the suppression of 'observation noise' impact caused by the measurement system and unconsidered source errors. The proposed method that is pertinently developed to improve the accuracy of the targeted robot, is implemented as follows: with the aid of screw theory, a linear map between the pose error twist of the end effector and all possible geometric source errors of the hybrid machining robot is formulated. Following the measurement and solution of pose error twist, an extended Kalman filter (EKF) is then employed to obtain more reliable and stable identification results of geometric errors. A linearized error compensator is then proposed and used in calibration experiments. Compared with the least square method, the proposed EKF method is proved to be robust and has good compensation effect in the verification configurations. After kinematic calibration, the positional and angular errors of the robot are reduced to be less than 60.6 gm and 67.6 grad respectively. The results verify the effectiveness and general applicability of the proposed identification and compensation strategy.
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