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Keywords = SmoteNC ctGAN

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14 pages, 1243 KiB  
Article
Designing a Hybrid Equipment-Failure Diagnosis Mechanism under Mixed-Type Data with Limited Failure Samples
by Cheng-Hui Chen, Chen-Kun Tsung and Shyr-Shen Yu
Appl. Sci. 2022, 12(18), 9286; https://doi.org/10.3390/app12189286 - 16 Sep 2022
Cited by 13 | Viewed by 2793
Abstract
The rarity of equipment failures results in a high level of imbalance between failure data and normal operation data, which makes the effective classification and prediction of such data difficult. Furthermore, many failure data are dominated by mixed data, which makes the model [...] Read more.
The rarity of equipment failures results in a high level of imbalance between failure data and normal operation data, which makes the effective classification and prediction of such data difficult. Furthermore, many failure data are dominated by mixed data, which makes the model unable to adapt to this type of failure problem. Second, the replacement cycle of production equipment increases the difficulty of collecting failure data. In this paper, an equipment failure diagnosis method is proposed to solve the problem of poor prediction accuracy due to limited data. In this method, the synthetic minority oversampling technique is combined with a conditional tabular generative adversarial network. The proposed method can be used to predict limited data with a mixture of numerical and categorical data. Experimental results indicate that the proposed method can improve 6.45% compared to other similar methods when equipment failure data account for less than 1% of the total data. Full article
(This article belongs to the Special Issue Edge Computing with AI)
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