Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like imag...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9078761/ |
| _version_ | 1818557455883304960 |
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| author | Dhiraj Neupane Jongwon Seok |
| author_facet | Dhiraj Neupane Jongwon Seok |
| author_sort | Dhiraj Neupane |
| collection | DOAJ |
| description | A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. |
| first_indexed | 2024-12-13T23:59:43Z |
| format | Article |
| id | doaj.art-56d57199d65a4f5aa66158eb75b99f9a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:59:43Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56d57199d65a4f5aa66158eb75b99f9a2022-12-21T23:26:24ZengIEEEIEEE Access2169-35362020-01-018931559317810.1109/ACCESS.2020.29905289078761Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A ReviewDhiraj Neupane0https://orcid.org/0000-0001-6548-311XJongwon Seok1https://orcid.org/0000-0002-5723-6386Department of Information and Communication Engineering, Changwon National University, Changwon, South KoreaDepartment of Information and Communication Engineering, Changwon National University, Changwon, South KoreaA smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.https://ieeexplore.ieee.org/document/9078761/Bearingdeep learningmachine learningmachinery fault detection and diagnosisCWRU dataset |
| spellingShingle | Dhiraj Neupane Jongwon Seok Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review IEEE Access Bearing deep learning machine learning machinery fault detection and diagnosis CWRU dataset |
| title | Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review |
| title_full | Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review |
| title_fullStr | Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review |
| title_full_unstemmed | Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review |
| title_short | Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review |
| title_sort | bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches a review |
| topic | Bearing deep learning machine learning machinery fault detection and diagnosis CWRU dataset |
| url | https://ieeexplore.ieee.org/document/9078761/ |
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