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 is a...
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2011
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Online Access: | http://eprints.utem.edu.my/id/eprint/20107/1/APPLICATION%20OF%20MULTI-STEP%20TIME%20SERIES%20PREDICTION%20FOR%20INDUSTRIAL%20EQUIPMENT%20PROGNOSTIC-SITI%20AZIRAH%20ASMAI-MAK%2000346%20RAF.pdf http://eprints.utem.edu.my/id/eprint/20107/ https://core.ac.uk/download/pdf/235629015.pdf |
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my.utem.eprints.201072021-07-11T16:33:45Z http://eprints.utem.edu.my/id/eprint/20107/ Application Of Multi- Step Time Series Prediction For Industrial Equipment Prognostic Asmai, Siti Azirah Abdullah, Rosmiza Wahida Che Soh, Mohd Norhisham Hasan Basari, Abd Samad Hussin, Burairah 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 text en http://eprints.utem.edu.my/id/eprint/20107/1/APPLICATION%20OF%20MULTI-STEP%20TIME%20SERIES%20PREDICTION%20FOR%20INDUSTRIAL%20EQUIPMENT%20PROGNOSTIC-SITI%20AZIRAH%20ASMAI-MAK%2000346%20RAF.pdf Asmai, Siti Azirah and Abdullah, Rosmiza Wahida and Che Soh, Mohd Norhisham and Hasan Basari, Abd Samad and Hussin, Burairah (2011) Application Of Multi- Step Time Series Prediction For Industrial Equipment Prognostic. In: IEEE Conference On Open System (ICOS2011), 25-28 September 2011, Langkawi, Kedah. https://core.ac.uk/download/pdf/235629015.pdf |
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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 |
author |
Asmai, Siti Azirah Abdullah, Rosmiza Wahida Che Soh, Mohd Norhisham Hasan Basari, Abd Samad Hussin, Burairah |
spellingShingle |
Asmai, Siti Azirah Abdullah, Rosmiza Wahida Che Soh, Mohd Norhisham Hasan Basari, Abd Samad Hussin, Burairah Application Of Multi- Step Time Series Prediction For Industrial Equipment Prognostic |
author_facet |
Asmai, Siti Azirah Abdullah, Rosmiza Wahida Che Soh, Mohd Norhisham Hasan Basari, Abd Samad Hussin, Burairah |
author_sort |
Asmai, Siti Azirah |
title |
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_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_sort |
application of multi- step time series prediction for industrial equipment prognostic |
publishDate |
2011 |
url |
http://eprints.utem.edu.my/id/eprint/20107/1/APPLICATION%20OF%20MULTI-STEP%20TIME%20SERIES%20PREDICTION%20FOR%20INDUSTRIAL%20EQUIPMENT%20PROGNOSTIC-SITI%20AZIRAH%20ASMAI-MAK%2000346%20RAF.pdf http://eprints.utem.edu.my/id/eprint/20107/ https://core.ac.uk/download/pdf/235629015.pdf |
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