Enhancing robotic manipulator fault detection with advanced machine learning techniques

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dc.authoridKhan, Faiq Ahmad/0000-0002-1275-6505
dc.contributor.authorKhan, Faiq Ahmad
dc.contributor.authorJamil, Akhtar
dc.contributor.authorKhan, Shaiq Ahmad
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2024-05-19T14:41:27Z
dc.date.available2024-05-19T14:41:27Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe optimization of rotating machinery processes is crucial for enhanced industrial productivity. Automatic machine health monitoring systems play a vital role in ensuring smooth operations. This study introduces a novel approach for fault diagnosis in robotic manipulators through motor sound analysis to enhance industrial efficiency and prevent machinery downtime. A unique dataset is generated using a custom robotic manipulator to examine the effectiveness of both deep learning and traditional machine learning in identifying motor anomalies. The investigation includes a two-stage analysis, initially leveraging 2D spectrogram features with neural network architectures, followed by an evaluation of 1D MFCC features using various conventional machine learning algorithms. The results reveal that the proposed custom CNN and 1D-CNN models significantly surpass traditional methods, achieving an F1-score exceeding 92%, highlighting the potential of sound analysis for automated fault detection in robotic systems. Additional experiments were carried out to investigate 1D MFCC features with various machine learning algorithms, including KNN, DT, LR, RF, SVM, MLP, and 1D-CNN. Augmented with additional data collected from the locally designed manipulator, our experimental setup significantly enhances model performance. Particularly, the 1D-CNN stands out as the top-performing model on the augmented dataset.en_US
dc.identifier.doi10.1088/2631-8695/ad3dae
dc.identifier.issn2631-8695
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85191323595en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org10.1088/2631-8695/ad3dae
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5111
dc.identifier.volume6en_US
dc.identifier.wosWOS:001207458800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIop Publishing Ltden_US
dc.relation.ispartofEngineering Research Expressen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFaults Diagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectTransfer Learningen_US
dc.subjectMotor Soundsen_US
dc.titleEnhancing robotic manipulator fault detection with advanced machine learning techniquesen_US
dc.typeArticleen_US

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