Concise convolutional neural network model for fault detection
Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Komunitas Ilmuwan dan Profesional Muslim Indonesia
2022-07-01
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| Series: | Communications in Science and Technology |
| Subjects: | |
| Online Access: | https://cst.kipmi.or.id/journal/article/view/746 |
| _version_ | 1828526107584888832 |
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| author | Muhammad Al Firdausi Shafiq Ahmad |
| author_facet | Muhammad Al Firdausi Shafiq Ahmad |
| author_sort | Muhammad Al Firdausi |
| collection | DOAJ |
| description | Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs. |
| first_indexed | 2024-12-11T21:17:34Z |
| format | Article |
| id | doaj.art-1339eb18129243ab9627d6be9009813b |
| institution | Directory Open Access Journal |
| issn | 2502-9258 2502-9266 |
| language | English |
| last_indexed | 2024-12-11T21:17:34Z |
| publishDate | 2022-07-01 |
| publisher | Komunitas Ilmuwan dan Profesional Muslim Indonesia |
| record_format | Article |
| series | Communications in Science and Technology |
| spelling | doaj.art-1339eb18129243ab9627d6be9009813b2022-12-22T00:50:32ZengKomunitas Ilmuwan dan Profesional Muslim IndonesiaCommunications in Science and Technology2502-92582502-92662022-07-0171627210.21924/cst.7.1.2022.746746Concise convolutional neural network model for fault detectionMuhammad Al Firdausi0Shafiq Ahmad1King Saud University, Riyadh, Saudi ArabiaKing Saud University, Riyadh, Saudi ArabiaFault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.https://cst.kipmi.or.id/journal/article/view/746fault detectionball bearingdeep learningconvolutional neural networkvibration |
| spellingShingle | Muhammad Al Firdausi Shafiq Ahmad Concise convolutional neural network model for fault detection Communications in Science and Technology fault detection ball bearing deep learning convolutional neural network vibration |
| title | Concise convolutional neural network model for fault detection |
| title_full | Concise convolutional neural network model for fault detection |
| title_fullStr | Concise convolutional neural network model for fault detection |
| title_full_unstemmed | Concise convolutional neural network model for fault detection |
| title_short | Concise convolutional neural network model for fault detection |
| title_sort | concise convolutional neural network model for fault detection |
| topic | fault detection ball bearing deep learning convolutional neural network vibration |
| url | https://cst.kipmi.or.id/journal/article/view/746 |
| work_keys_str_mv | AT muhammadalfirdausi conciseconvolutionalneuralnetworkmodelforfaultdetection AT shafiqahmad conciseconvolutionalneuralnetworkmodelforfaultdetection |