Equipment Fault Prognosis Based on Temporal Association Rules
Equipment fault prognosis is important for reliability, operational safety, and efficient performance of equipment. Temporal fault data model is built according to the principles of the Apriori traditional association rules algorithm based on the characteristics of fault data. An Improved Apriori al...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2014-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02111 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs3198_1329 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a GAN, Chao |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a LU, Yuan |e author |
| 700 | 1 | 0 | |a HU, Ying |e author |
| 700 | 1 | 0 | |a GU, Jia |e author |
| 700 | 1 | 0 | |a QIU, Xin |e author |
| 245 | 0 | 0 | |a Equipment Fault Prognosis Based on Temporal Association Rules |
| 260 | |b Institute of Advanced Engineering and Science, |c 2014-03-01. | ||
| 520 | |a Equipment fault prognosis is important for reliability, operational safety, and efficient performance of equipment. Temporal fault data model is built according to the principles of the Apriori traditional association rules algorithm based on the characteristics of fault data. An Improved Apriori algorithm and frequent temporal association rules algorithm are proposed in this study by converting fault data to temporal item sets matrix. Equipment fault trends are predicted by mining the frequent temporal association rules of fault data based on the algorithm, which provides good support for equipment maintenance and management. At last an example is given to prove the feasibility and practical application of proposed algorithms DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4563 | ||
| 540 | |a Copyright (c) 2013 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a School of Mechanical Engineering，Nanchang University | ||
| 690 | |a Fault Prognosis; Temporal Association Rules; Apriori algorithm; Data Mining ;Frequent Item sets; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 3: March 2014; 1832-1841 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v12.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3198/1329 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3198/1329 |z Get fulltext |