Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting Article Swipe
Chen Yang
,
Chuang Peng
,
Lei Chen
,
Kuangrong Hao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad0f0f
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad0f0f
The polyester industry is a complex process industry, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data. The time-series data collected from sensors cross-production-line often exhibit varying distributions. Current domain adaptation (DA) approaches in data-driven time series forecasting primarily concentrate on adjusting either the features or the models, neglecting the intricacies of industrial time series data. Furthermore, constructing deep neural networks for industrial data necessitates substantial computational resources and runtime due to their large and high-dimensional nature. In order to tackle these obstacles, we propose a novel Multi-level deep domain adaptive adversarial model based on tensor-train decomposition (TT-MDAM). Our model aims to strike a dynamic balance between prediction accuracy and runtime efficiency. By integrating multiple perspectives at the feature, trend, and model levels, we leverage DA to enhance the prediction accuracy of our model in the target domain. Additionally, by analyzing the weight matrix of the neural network, we generate a low-rank model to improve operational efficiency. The application of the proposed TT-MDAM approach to both the three-phase flow facility process (TPFF) dataset and a real-world polyester esterification process dataset reveals promising results, outperforming state-of-the-art methodologies in terms of prediction performance. The results indicate that the approach provides a viable solution for building time series prediction models in industrial processes with new equipment or production lines.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad0f0f
- OA Status
- hybrid
- Cited By
- 3
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388892236
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388892236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6501/ad0f0fDigital Object Identifier
- Title
-
Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecastingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-22Full publication date if available
- Authors
-
Chen Yang, Chuang Peng, Lei Chen, Kuangrong HaoList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ad0f0fPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6501/ad0f0fDirect OA link when available
- Concepts
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Computer science, Leverage (statistics), Time series, Process (computing), Artificial neural network, Artificial intelligence, Machine learning, Domain (mathematical analysis), Data mining, Industrial engineering, Engineering, Operating system, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4323922461, https://openalex.org/W4386936028, https://openalex.org/W4319996939, https://openalex.org/W4375928691, https://openalex.org/W4313612460, https://openalex.org/W4302760263, https://openalex.org/W3181153667, https://openalex.org/W2942287057, https://openalex.org/W4320481403, https://openalex.org/W4309554163, https://openalex.org/W3137724529, https://openalex.org/W4289522624, https://openalex.org/W3093957891, https://openalex.org/W4323536861, https://openalex.org/W4382937402, https://openalex.org/W4313254917, https://openalex.org/W4285820068, https://openalex.org/W4306146777, https://openalex.org/W3080683114, https://openalex.org/W4296205652, https://openalex.org/W3036463503, https://openalex.org/W4378194787, https://openalex.org/W3030350388, https://openalex.org/W4284886155, https://openalex.org/W3211333123, https://openalex.org/W4312737473, https://openalex.org/W2906239552, https://openalex.org/W4353045564, https://openalex.org/W4226486702, https://openalex.org/W3094539536, https://openalex.org/W4285297899, https://openalex.org/W4293259034, https://openalex.org/W3110275472, https://openalex.org/W4288735300, https://openalex.org/W4387242722, https://openalex.org/W4293242646, https://openalex.org/W4361212982, https://openalex.org/W3165113810, https://openalex.org/W6638060716, https://openalex.org/W3092004705, https://openalex.org/W4320015860, https://openalex.org/W2989402453, https://openalex.org/W6764409202, https://openalex.org/W4383371592, https://openalex.org/W3016182930, https://openalex.org/W6770717842, https://openalex.org/W567350711, https://openalex.org/W4200516166, https://openalex.org/W3197588717, https://openalex.org/W4327748143, https://openalex.org/W1798945469, https://openalex.org/W3093314218, https://openalex.org/W3035524453, https://openalex.org/W2952902402 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 5, 10, 29, 102, 120, 167, 191, 215 |
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| abstract_inverted_index.DA | 142 |
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| abstract_inverted_index.we | 100, 140, 165 |
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| abstract_inverted_index.The | 1, 34, 174, 208 |
| abstract_inverted_index.and | 85, 91, 126, 137, 190 |
| abstract_inverted_index.can | 24 |
| abstract_inverted_index.due | 27, 87 |
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| abstract_inverted_index.new | 16, 22, 228 |
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| abstract_inverted_index.the | 60, 63, 66, 134, 145, 152, 158, 162, 177, 183, 212 |
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| abstract_inverted_index.data | 36, 80 |
| abstract_inverted_index.deep | 75, 105 |
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| abstract_inverted_index.from | 38 |
| abstract_inverted_index.lack | 30 |
| abstract_inverted_index.that | 211 |
| abstract_inverted_index.time | 11, 52, 70, 220 |
| abstract_inverted_index.with | 21, 227 |
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| abstract_inverted_index.data. | 33, 72 |
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| abstract_inverted_index.these | 98 |
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| abstract_inverted_index.lines. | 232 |
| abstract_inverted_index.matrix | 160 |
| abstract_inverted_index.models | 223 |
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| abstract_inverted_index.TT-MDAM | 179 |
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| abstract_inverted_index.complex | 6 |
| abstract_inverted_index.dataset | 189, 196 |
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| abstract_inverted_index.enhance | 144 |
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| abstract_inverted_index.levels, | 139 |
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| abstract_inverted_index.reveals | 197 |
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| abstract_inverted_index.sensors | 23, 39 |
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| abstract_inverted_index.promising | 198 |
| abstract_inverted_index.resources | 84 |
| abstract_inverted_index.(TT-MDAM). | 114 |
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| abstract_inverted_index.historical | 32 |
| abstract_inverted_index.industrial | 69, 79, 225 |
| abstract_inverted_index.neglecting | 65 |
| abstract_inverted_index.obstacles, | 99 |
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| corresponding_author_ids | https://openalex.org/A5100719382, https://openalex.org/A5100671215 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I181326427 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.64936713 |
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| citation_normalized_percentile.is_in_top_10_percent | False |