Analytical calculation of inductances under stator inter-turn short circuits fault condition in operating squirrel cage induction motors

Touil, A.; Babaa, Fatima; Ouamane, M; Bennis, Ouafae; Kratz, Frédéric; Hadjami, Marouane

doi:10.1109/icsc50472.2021.9666577

Cited by 3 publications

(2 citation statements)

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“…Study of electrical quantities for detection and diagnosis electrical faults like unbalanced supply voltage [4], inter-turn short-circuit faults [8] or a rotor winding problems, is more appropriate and adequate to propose effective fault indicators. However, we notice that mechanical faults have a more particular influence on mechanical quantities which are the same nature [7].…”

Section: Figure 5 Test Benchmentioning

confidence: 99%

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Bearing Fault Diagnosis in Induction Machines Based on Electromagnetic Torque Spectral Frequencies Analysis

Touil,

Babaa,

Kratz

et al. 2024

JESA

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The main aim of this article is to achieve predictive maintenance by proposing reliable residuals specific to outer ring bearing faults. Our work falls within the general context of maintenance, with particular emphasis on vibration analysis techniques. For an in-depth study of the fault, we use spectral analysis of electromagnetic torque, which gives very satisfactory results compared with work based on line current or neutral voltage fault signatures due to the similarity in the mechanical nature of the fault and the signal to be studied. In fact, an analytical calculation of the electromagnetic torque has been developed to obtain the specific frequencies of the fault under consideration. To highlight our results, the simulation of the analytical calculation of this fault was implemented in MATLAB/SIMULINK using the Fast Fourier Transform method to extract fault signatures. The induction motor's performance was analyzed under various operating conditions, including both healthy and faulty states. Finally, an experimental study will support our analytical developments.

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Section: Figure 5 Test Benchmentioning

confidence: 99%

“…A study of asynchronous motor malfunctions has shown that they are classified according to their nature. We distinguish: Bearing faults at 41% [5,6], stator faults at 37% [7,8], rotor faults at 10% [9,10], and other faults at 12% [11,12].…”

Section: Introductionmentioning

confidence: 99%