Semantic Hybrid Signal Temporal Logic Learning-Based Data-Driven Anomaly Detection in the Textile Process Article Swipe
The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-series data collected from a representative textile plant in Zhejiang Province, China. Comparative experimental results demonstrated the feasibility of the proposed method.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr11092804
- https://www.mdpi.com/2227-9717/11/9/2804/pdf?version=1695281692
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386934878
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386934878Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/pr11092804Digital Object Identifier
- Title
-
Semantic Hybrid Signal Temporal Logic Learning-Based Data-Driven Anomaly Detection in the Textile ProcessWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-21Full publication date if available
- Authors
-
Xu Huo, Kuangrong HaoList of authors in order
- Landing page
-
https://doi.org/10.3390/pr11092804Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9717/11/9/2804/pdf?version=1695281692Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-9717/11/9/2804/pdf?version=1695281692Direct OA link when available
- Concepts
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Anomaly detection, Computer science, Data mining, Process (computing), Inference, Redundancy (engineering), Time series, Anomaly (physics), Artificial intelligence, SIGNAL (programming language), Machine learning, Physics, Operating system, Condensed matter physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.determined, | 107 |
| abstract_inverted_index.development | 1 |
| abstract_inverted_index.effectively | 63 |
| abstract_inverted_index.feasibility | 157 |
| abstract_inverted_index.operations. | 69 |
| abstract_inverted_index.production. | 14 |
| abstract_inverted_index.time-series | 140 |
| abstract_inverted_index.Furthermore, | 70 |
| abstract_inverted_index.constructed, | 116 |
| abstract_inverted_index.constructing | 54 |
| abstract_inverted_index.demonstrated | 155 |
| abstract_inverted_index.experimental | 153 |
| abstract_inverted_index.insufficient | 90 |
| abstract_inverted_index.interpretation | 76 |
| abstract_inverted_index.representative | 145 |
| abstract_inverted_index.specifications | 57, 102 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5001762132 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I181326427 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.14006366 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |