Condition Monitoring and Fault Diagnosis of Induction Motor - An Experimental Analysis

Almounajjed, Abdelelah; Sahoo, Ashwin Kumar; Kumar, Mani Kant; Bakro, Mhamad

doi:10.1109/icees51510.2021.9383729

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The ITSC fault was simulated using the Clarks-Concordia transformation via Matlab/Simulink. To simulate the incipient fault, Phase-C turns are gradually shorted by (1,3,5,7,10,13,15,17,20 The detailed coefficients at level 7 were discriminated, and the statistical parameters, L 1 and L 2 norms, were calculated from the coefficients for the three phases. The acquisition time was 6 minutes.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Phase-C is considered the faulty phase, which was taken earlier for Simulink. The machine winding was modified and specially wound by adding 17 tappings on the stator coils of Phase C such that the number of defective turns is (1,3,5,7,10,13,15,17,20,23,25,35,45,55,65 an ITSC fault with various numbers of turns in the faulty phase, as depicted in Fig. 12.…”

Section: Resultsmentioning

confidence: 99%

“…Induction motors (IMs) with capacities ranging from a few watts to megawatts are employed as leading movers, and they play an essential role in recent industries. Owing to their robustness, reliability, and low maintenance costs, IMs have received increasing attention in automotive, electrical vehicle traction, and power conversion systems [1][2][3] . Despite its favorable characteristics, the induction motor is subjected to mechanical and electrical faults that can be caused by prolonged operation, current and voltage imbalance, and harsh operating conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Almounajjed

Sahoo

Kumar

et al. 2023

Chin. J. Electr. Eng.

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A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L 1 and L 2 norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L 1 and L 2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

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