Model-based broken rotor bars fault detection and diagnosis in squirrel-cage induction motors

Duvvuri, Sssr Sarathbabu; Detroja, Ketan P.

doi:10.1109/systol.2016.7739804

Cited by 13 publications

(3 citation statements)

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“…The four terms resistances of broken rotor bar fault (Fig. 4) are given by: (10) In this expression, the summation is applied to all bars with fault. Rbfk is the resistance of the bar index k from its initial value before the fault.…”

Section: Broken Rotor Bar Faultmentioning

confidence: 99%

“…The tasks of detection and diagnosis of failures are naturally found their place in the monitoring system at closed-loop of IM. These tasks related to the knowledge acquired on the encountered problems, which make the fault detection and diagnosis approaches divided into two wide categories: approach with model [10] and approach without model [11].…”

Section: Introductionmentioning

confidence: 99%

“…Figs 9,10,11,12 and Table1show that the residual currents start with a very small value in a healthy state, socalled the threshold ||rIa || = 0.1 A, then increase abruptly when the broken rotor bar, static air-gap eccentricity or mixed fault occur at t=1s according to the fault types and their severities. This technique can be considered as a reliable indicator for rapid incipient faults detection, even at low slip.…”

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confidence: 99%

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Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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Section: Broken Rotor Bar Faultmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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