FEM analysis and experiments of a Double-sided Axial Flux Switched Reluctance Motor for two alternative phase winding terminal connections

Pal, Sabarni; Sengupta, Mainak

doi:10.1109/spices52834.2022.9774191

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Numerical models exploiting FEM are also applied to the study of electromagnetic phenomena in electrical machines. Field and field circuit models exploiting FEM have been successfully applied in the analysis and synthesis of many different types of electromechanical transducers, such as induction [12][13][14] and synchronous machines [15][16][17], switched reluctance motors [18][19][20], stepper motors, brushless direct current machines, and linear motors, as well as in wireless power transfer systems, magnetic field excitation systems, and other electromagnetic converters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Pietrowski,

Górny

2024

Energies

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The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.

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