DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE
The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2024-03-01
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| Series: | Proceedings on Engineering Sciences |
| Subjects: | |
| Online Access: | https://pesjournal.net/journal/v6-n1/31.pdf |
| _version_ | 1797248083764969472 |
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| author | Sneha Kashyap P. S. Raghavendra Rao Pavan Chaudhary Savita Yadav |
| author_facet | Sneha Kashyap P. S. Raghavendra Rao Pavan Chaudhary Savita Yadav |
| author_sort | Sneha Kashyap |
| collection | DOAJ |
| description | The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness. |
| first_indexed | 2024-04-24T20:08:57Z |
| format | Article |
| id | doaj.art-871a080b9c0d458abc7a41d8afe56a29 |
| institution | Directory Open Access Journal |
| issn | 2620-2832 2683-4111 |
| language | English |
| last_indexed | 2024-04-24T20:08:57Z |
| publishDate | 2024-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj.art-871a080b9c0d458abc7a41d8afe56a292024-03-23T15:03:37ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112024-03-01628129010.24874/PES.SI.24.02.011DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUESneha Kashyap0https://orcid.org/0000-0003-0276-9449P. S. Raghavendra Rao1https://orcid.org/0000-0001-6540-7344Pavan Chaudhary2https://orcid.org/0009-0000-7596-2341Savita Yadav 3https://orcid.org/0000-0001-6528-7290Arka Jain University, Jamshedpur, Jharkhand, IndiaJAIN (Deemed-to-be University), Ramanagara District, Karnataka, IndiaMaharishi University of Information Technology, Uttar Pradesh, IndiaNoida Institute of Engineering & Technology, IndiaThe rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.https://pesjournal.net/journal/v6-n1/31.pdfrolling element bearingsdefect recognitionrotating machinemachine learning (ml)mechanical equipment |
| spellingShingle | Sneha Kashyap P. S. Raghavendra Rao Pavan Chaudhary Savita Yadav DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE Proceedings on Engineering Sciences rolling element bearings defect recognition rotating machine machine learning (ml) mechanical equipment |
| title | DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE |
| title_full | DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE |
| title_fullStr | DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE |
| title_full_unstemmed | DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE |
| title_short | DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE |
| title_sort | defect recognition and classification in rolling element bearings using a novel machine learning technique |
| topic | rolling element bearings defect recognition rotating machine machine learning (ml) mechanical equipment |
| url | https://pesjournal.net/journal/v6-n1/31.pdf |
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