Predicting remaining useful life of rotating machinery based artificial neural network
Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpos...
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| Format: | Article |
| Language: | en |
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Elsevier Science Ltd
2010
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| Online Access: | http://eprints.uthm.edu.my/7838/1/J5101_2a145de553e3afa629ab06769133ea86.pdf http://eprints.uthm.edu.my/7838/ https://doi.org/10.1016/j.camwa.2010.03.065 |
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| author | Mahamad, Abd Kadir Saon, Sharifah Hiyama, Takashi |
| author_facet | Mahamad, Abd Kadir Saon, Sharifah Hiyama, Takashi |
| author_sort | Mahamad, Abd Kadir |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Accurate remaining useful life (RUL) prediction of machines is important for condition
based maintenance (CBM) to improve the reliability and cost of maintenance. This paper
proposes artificial neural network (ANN) as a method to improve accurate RUL prediction
of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull
hazard rates of root mean square (RMS) and kurtosis from its present and previous points
as input. Meanwhile, the normalized life percentage is selected as output. By doing that,
the noise of a degradation signal from a target bearing can be minimized and the accuracy
of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural
Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the
proposed method shows that better performance is achieved in order to predict bearing
failure. |
| format | Article |
| id | my.uthm.eprints-7838 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2010 |
| publisher | Elsevier Science Ltd |
| record_format | eprints |
| spelling | my.uthm.eprints-78382022-10-12T03:11:44Z http://eprints.uthm.edu.my/7838/ Predicting remaining useful life of rotating machinery based artificial neural network Mahamad, Abd Kadir Saon, Sharifah Hiyama, Takashi T Technology (General) Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure. Elsevier Science Ltd 2010 Article PeerReviewed text en http://eprints.uthm.edu.my/7838/1/J5101_2a145de553e3afa629ab06769133ea86.pdf Mahamad, Abd Kadir and Saon, Sharifah and Hiyama, Takashi (2010) Predicting remaining useful life of rotating machinery based artificial neural network. Computers and Mathematics with Applications, 60. pp. 1078-1087. ISSN 0898-1221 https://doi.org/10.1016/j.camwa.2010.03.065 |
| spellingShingle | T Technology (General) Mahamad, Abd Kadir Saon, Sharifah Hiyama, Takashi Predicting remaining useful life of rotating machinery based artificial neural network |
| title | Predicting remaining useful life of rotating machinery based artificial neural network |
| title_full | Predicting remaining useful life of rotating machinery based artificial neural network |
| title_fullStr | Predicting remaining useful life of rotating machinery based artificial neural network |
| title_full_unstemmed | Predicting remaining useful life of rotating machinery based artificial neural network |
| title_short | Predicting remaining useful life of rotating machinery based artificial neural network |
| title_sort | predicting remaining useful life of rotating machinery based artificial neural network |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7838/1/J5101_2a145de553e3afa629ab06769133ea86.pdf http://eprints.uthm.edu.my/7838/ https://doi.org/10.1016/j.camwa.2010.03.065 |
| url_provider | http://eprints.uthm.edu.my/ |