Novel Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection

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dc.authoridAnayi, Fatih/0000-0001-8408-7673
dc.authoridALOMARI, OSAMA/0000-0002-1135-5750
dc.authoridPackianather, Michael/0000-0002-9436-8206
dc.contributor.authorIbrahim, Alasmer
dc.contributor.authorAnayi, Fatih
dc.contributor.authorPackianather, Michael
dc.contributor.authorAl-Omari, Osama
dc.date.accessioned2024-09-11T19:51:58Z
dc.date.available2024-09-11T19:51:58Z
dc.date.issued2021
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description56th International Universities Power Engineering Conference (UPEC) - Powering Net Zero Emissions -- AUG 31-SEP 03, 2021 -- Teesside Univ, ELECTR NETWORKen_US
dc.description.abstractFault diagnosis of anomalies in induction motors is essential to ensure industry safety. This paper presents a new hybrid Invasive Weed Optimization and Machine Learning approach for fault diagnosis in an induction motor. The vibration signal provides a lot of information about the motor's operating conditions. Therefore, the vibration signal of the motor was chosen to investigate the fault diagnosis. Two identical 400-V, 50-Hz, 4-pole 0.75 HP induction motors were under healthy, mechanical, and electrical faults tested in a laboratory with different loading. A hybrid model was developed using the vibration signal, the Invasive Weed Optimization algorithm (IWO), and machine learning classifiers. Some statistical features were extracted from the signal using Discrete Wavelet Transform (DWT). The invasive weed optimization algorithm (IWO) was utilized to reduce the number of the extracted features and select the most suitable ones. Then, three classification algorithms namely k-Nearest Neighbor neural network (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained using k-fold cross-validation and tested to predict the true class. The advantage of combining these techniques is to reduce the training time and increase the average accuracy of the model. The performance of the proposed fault diagnosis model was evaluated by measuring the Specificity, Accuracy, Precision, Recall, and F1_score. The experimental results prove that the proposed model has achieved more than 99.90% of accuracy. Furthermore, the other evaluation parameters also show the same representation of performance. The hybrid model has proved successfully its robust for diagnosing the faults under different load conditions.en_US
dc.description.sponsorshipIEEE,IEEE United Kingdom & Ireland Sect,IEEE Power & Energy Soc,Inst Engn & Technol,Lucas Nulle,MDPI, Elect Journal,MDPI, Energies Journalen_US
dc.description.sponsorshipHigh Ministry of Education in Libyaen_US
dc.description.sponsorshipAlasmer Ibrahim acknowledges the sponsorship from the High Ministry of Education in Libya.en_US
dc.identifier.doi10.1109/UPEC50034.2021.9548171
dc.identifier.isbn978-1-6654-4389-0
dc.identifier.scopus2-s2.0-85116668802en_US
dc.identifier.urihttps://doi.org/10.1109/UPEC50034.2021.9548171
dc.identifier.urihttps://hdl.handle.net/11363/7879
dc.identifier.wosWOS:000723608400022en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2021 56th International Universities Power Engineering Conference (Upec 2021): Powering Net Zero Emissionsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectFault Diagnosisen_US
dc.subjectInduction Motoren_US
dc.subjectMachine Learning Classifiersen_US
dc.subjectDiscrete Wavelet Transform (DWT)en_US
dc.subjectInvasive Weed Optimization Algorithm (IWO)en_US
dc.titleNovel Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detectionen_US
dc.typeConference Objecten_US

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