Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques Article Swipe
William Villegas-Ch
,
Jaime Govea
,
Walter Gaibor-Naranjo
,
Santiago Sánchez-Viteri
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2024.1398126
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2024.1398126
In the contemporary realm of industry, the imperative for influential and steadfast systems to detect anomalies is critically recognized. Our study introduces a cutting-edge approach utilizing a deep learning model of the Long-Short Term Memory variety, meticulously crafted for real-time surveillance and mitigation of irregularities within industrial settings. Through the careful amalgamation of data acquisition and analytic processing informed by our model, we have forged a system adept at pinpointing anomalies with high precision, capable of autonomously proposing or implementing remedial measures. The findings demonstrate a marked enhancement in the efficacy of operations, with the model’s accuracy surging to 95%, recall at 90%, and an F1 score reaching 92.5%. Moreover, the system has favorably impacted the environment, evidenced by a 25% decline in CO2 emissions and a 20% reduction in water usage. Our model surpasses preceding systems, showcasing significant gains in speed and precision. This research corroborates the capabilities of deep learning within the industrial sector. It underscores the role of automated systems in fostering more sustainable and efficient operations in the contemporary industrial landscape.
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- Type
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- Language
- en
- Landing Page
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- OA Status
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- References
- 36
- Related Works
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- OpenAlex ID
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Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniquesWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-06Full publication date if available
- Authors
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William Villegas-Ch, Jaime Govea, Walter Gaibor-Naranjo, Santiago Sánchez-ViteriList of authors in order
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https://doi.org/10.3389/frai.2024.1398126Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/frai.2024.1398126Direct OA link when available
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Realm, Variety (cybernetics), Deep learning, Computer science, Artificial intelligence, Remedial education, Enhanced Data Rates for GSM Evolution, Industrial engineering, Data science, Machine learning, Engineering, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| primary_location.raw_source_name | Frontiers in Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.3389/frai.2024.1398126 |
| publication_date | 2024-11-06 |
| publication_year | 2024 |
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