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Article

New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection †

1
Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
2
High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
3
MLALP Research Group, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in IEEE 2021 56th International Universities Power Engineering Conference (UPEC), Middlesbrough, UK, 31 August–3 September 2021; pp. 1–6.
Academic Editor: Ahmed Abu-Siada
Energies 2022, 15(4), 1488; https://doi.org/10.3390/en15041488
Received: 30 December 2021 / Revised: 10 February 2022 / Accepted: 14 February 2022 / Published: 17 February 2022
Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions. View Full-Text
Keywords: fault diagnosis; induction motor; machine learning classifiers; discrete wavelet transform (DWT); invasive weed optimization algorithm (IWO); genetic algorithm (GA) fault diagnosis; induction motor; machine learning classifiers; discrete wavelet transform (DWT); invasive weed optimization algorithm (IWO); genetic algorithm (GA)
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MDPI and ACS Style

Ibrahim, A.; Anayi, F.; Packianather, M.; Alomari, O.A. New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection. Energies 2022, 15, 1488. https://doi.org/10.3390/en15041488

AMA Style

Ibrahim A, Anayi F, Packianather M, Alomari OA. New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection. Energies. 2022; 15(4):1488. https://doi.org/10.3390/en15041488

Chicago/Turabian Style

Ibrahim, Alasmer, Fatih Anayi, Michael Packianather, and Osama Ahmad Alomari. 2022. "New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection" Energies 15, no. 4: 1488. https://doi.org/10.3390/en15041488

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