Application of multi-step time series prediction for industrial equipment prognostic
The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/201/1/Prognosis-_ICOSIEEELangkawi.pdf http://eprints.utem.edu.my/id/eprint/201/ |
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| _version_ | 1832715942854918144 |
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| author | Asmai, Siti Azirah Abdullah, Rosmiza Wahida Hasan Basari, Abd Samad Hussin, Burairah |
| author_facet | Asmai, Siti Azirah Abdullah, Rosmiza Wahida Hasan Basari, Abd Samad Hussin, Burairah |
| author_sort | Asmai, Siti Azirah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The use of prognostics is critically to be
implemented in industrial. This paper presents an
application of multi-step time series prediction to support
industrial equipment prognostic. An artificial neural
network technique with sliding window is considered for the
multi-step prediction which is able to predict the series of
future equipment condition. The structure of prognostic
application is presented. The feasibility of this prediction
application was demonstrated by applying real condition
monitoring data of industrial equipment. |
| format | Conference or Workshop Item |
| id | my.utem.eprints-201 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2011 |
| record_format | eprints |
| spelling | my.utem.eprints-2012023-06-06T11:14:05Z http://eprints.utem.edu.my/id/eprint/201/ Application of multi-step time series prediction for industrial equipment prognostic Asmai, Siti Azirah Abdullah, Rosmiza Wahida Hasan Basari, Abd Samad Hussin, Burairah Q Science (General) The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of industrial equipment. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/201/1/Prognosis-_ICOSIEEELangkawi.pdf Asmai, Siti Azirah and Abdullah, Rosmiza Wahida and Hasan Basari, Abd Samad and Hussin, Burairah (2011) Application of multi-step time series prediction for industrial equipment prognostic. In: 2011 IEEE Conference on Open Systems, 25-28 Sept 2011, Langkawi, Malaysia. |
| spellingShingle | Q Science (General) Asmai, Siti Azirah Abdullah, Rosmiza Wahida Hasan Basari, Abd Samad Hussin, Burairah Application of multi-step time series prediction for industrial equipment prognostic |
| title | Application of multi-step time series prediction for industrial equipment prognostic |
| title_full | Application of multi-step time series prediction for industrial equipment prognostic |
| title_fullStr | Application of multi-step time series prediction for industrial equipment prognostic |
| title_full_unstemmed | Application of multi-step time series prediction for industrial equipment prognostic |
| title_short | Application of multi-step time series prediction for industrial equipment prognostic |
| title_sort | application of multi-step time series prediction for industrial equipment prognostic |
| topic | Q Science (General) |
| url | http://eprints.utem.edu.my/id/eprint/201/1/Prognosis-_ICOSIEEELangkawi.pdf http://eprints.utem.edu.my/id/eprint/201/ |
| url_provider | http://eprints.utem.edu.my/ |