Browsing by Author "Fagbolagun, O.S"

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    Acquisition of Current and Vibration Data for Rewound Burnt Three-Phase Induction Motor
    (2019) Abdullateef, A. I; Fagbolagun, O.S; Sanusi, M. F
    Induction motors are used in the industries for various applications because they are reliable and rugged. However, late detection of faults and inappropriate maintenance of the machine often leads to damage of the windings which results in production losses and outright replacement. This study presents the use of a data acquisition system (DAS) to acquire current and vibration data of re-designed and re-wound burnt three-phase induction motor for the purpose of the motor faults analysis. The designed number of turns per pole per phase is 253 turns which were distributed in four slots per phase at 64, 63, 63 and 63 turns respectively. The DAS developed was used to acquire the current and vibration data at both normal and fault conditions through tapings prepared for the purpose. The average value of normal current data in the red, yellow and blue phases from the sampled data are 3.4 A, 3.1 A and 2.7A respectively. The average currents during short circuit between phase A and phase B are 11.3 A and 12.2 A respectively while vibration fault data stands at 1.62. Also, for the inter-turn short circuit in phase A, the average value of the currents in the phases are 12.1A, 3.1A and 2.7A respectively. The Mean Square Error value of the acquired and measured data is 0.00002.
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    Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor
    (Journal of Applied Science & Environmental Management, 2020) Abdullateef, A. I; Fagbolagun, O.S; Sanusi, M. F; Akorede, M.F; Afolayan, M.A
    Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis.
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    Induction Motor Stator Fault Classification Using PCA-ANFIS Technique
    (ELEKTRIKA-Journal of Electrical Engineering,, 2020) Abdullateef, A. I; Sanusi, M.F; Fagbolagun, O.S
    Induction motors are used commonly for industrial operations due to their ease of operation coupled with ruggedness and reliability. However, they are subjected to stator faults which result in damage and consequently revenue losses. The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study. A burnt motor was redesigned and rewound while data acquisition was developed to acquire the current and vibration data needed for the fault classification. The data feature extraction for the fault classification was carried out by PCA while backpropagation and the least-squares algorithms were used for the training of the data. Three principal components, which severs as input for the ANFIS, were used to represent the entire data. The ANFIS was tested under four different paradigms, while the membership function type and epoch number were changed at each instant. The ANFIS model based on the triangular membership function and 10 epoch number was found appropriate and used, bringing the accuracy of the model to over 99% with the lowest ANFIS training RMSE error of 1.1795e-6. The ANFIS validation results of the fault classification show that the results are accurate, indicating that the PCAANFIS technique is applicable in fault diagnosis and classification of stator faults in induction motors.