Visual Analytics: A New Paradigm for Process Monitoring Article Swipe
Ibrahim Yousef
,
Sirish L. Shah
,
R. Bhushan Gopaluni
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2022.07.473
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2022.07.473
As a result of recent breakthroughs in computer vision technologies, significant research interest has emerged to encode process data into visual clues and treat process monitoring problems as computer vision tasks. Imaging time-series signals as a feature engineering step forms a new branch of data analytics called "visual analytics". In the context of process monitoring, we define visual analytics as the integration of visual representation of the data combined with the use of computer vision tools and analytical reasoning to support decision-making and knowledge extraction from the data. In this work, a novel end-to-end visual analytics pipeline for industrial process fault detection using 1D and 2D convolution operations is proposed. The proposed approach presents a visual representation of data that captures temporal and local features from historical time-series signals. Next, the learned features in a 2D format are visually recognized and classified using 2D convolution operations. Our experimental results demonstrate that this approach achieves better performance on an industrial multivariate dataset compared to other state-of-art signals imaging tools such as Gramian Angular Field (GAF) and Recurrence Plots (RP).
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2022.07.473
- OA Status
- diamond
- Cited By
- 4
- References
- 18
- Related Works
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- OpenAlex ID
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https://openalex.org/W4289878000Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2022.07.473Digital Object Identifier
- Title
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Visual Analytics: A New Paradigm for Process MonitoringWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Ibrahim Yousef, Sirish L. Shah, R. Bhushan GopaluniList of authors in order
- Landing page
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https://doi.org/10.1016/j.ifacol.2022.07.473Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.ifacol.2022.07.473Direct OA link when available
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Computer science, Visual analytics, Analytics, Process (computing), Artificial intelligence, Context (archaeology), Pipeline (software), Visualization, Data science, Data mining, Machine learning, Operating system, Biology, Programming language, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2024: 1, 2023: 3Per-year citation counts (last 5 years)
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18Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.imaging | 166 |
| abstract_inverted_index.learned | 131 |
| abstract_inverted_index.process | 17, 24, 53, 99 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.signals | 33, 165 |
| abstract_inverted_index.support | 80 |
| abstract_inverted_index.achieves | 153 |
| abstract_inverted_index.approach | 112, 152 |
| abstract_inverted_index.captures | 120 |
| abstract_inverted_index.combined | 68 |
| abstract_inverted_index.compared | 161 |
| abstract_inverted_index.computer | 7, 28, 73 |
| abstract_inverted_index.features | 124, 132 |
| abstract_inverted_index.interest | 12 |
| abstract_inverted_index.pipeline | 96 |
| abstract_inverted_index.presents | 113 |
| abstract_inverted_index.problems | 26 |
| abstract_inverted_index.proposed | 111 |
| abstract_inverted_index.research | 11 |
| abstract_inverted_index.signals. | 128 |
| abstract_inverted_index.temporal | 121 |
| abstract_inverted_index.visually | 138 |
| abstract_inverted_index.analytics | 45, 58, 95 |
| abstract_inverted_index.detection | 101 |
| abstract_inverted_index.knowledge | 83 |
| abstract_inverted_index.proposed. | 109 |
| abstract_inverted_index.reasoning | 78 |
| abstract_inverted_index.Recurrence | 175 |
| abstract_inverted_index.analytical | 77 |
| abstract_inverted_index.classified | 141 |
| abstract_inverted_index.end-to-end | 93 |
| abstract_inverted_index.extraction | 84 |
| abstract_inverted_index.historical | 126 |
| abstract_inverted_index.industrial | 98, 158 |
| abstract_inverted_index.monitoring | 25 |
| abstract_inverted_index.operations | 107 |
| abstract_inverted_index.recognized | 139 |
| abstract_inverted_index.analytics". | 48 |
| abstract_inverted_index.convolution | 106, 144 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.engineering | 37 |
| abstract_inverted_index.integration | 61 |
| abstract_inverted_index.monitoring, | 54 |
| abstract_inverted_index.operations. | 145 |
| abstract_inverted_index.performance | 155 |
| abstract_inverted_index.significant | 10 |
| abstract_inverted_index.time-series | 32, 127 |
| abstract_inverted_index.experimental | 147 |
| abstract_inverted_index.multivariate | 159 |
| abstract_inverted_index.state-of-art | 164 |
| abstract_inverted_index.breakthroughs | 5 |
| abstract_inverted_index.technologies, | 9 |
| abstract_inverted_index.representation | 64, 116 |
| abstract_inverted_index.decision-making | 81 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5089006794, https://openalex.org/A5043627103, https://openalex.org/A5008908321 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I141945490, https://openalex.org/I154425047 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.72187775 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |