The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The Industrial Internet of Things: Examining How the IIoT Will Improve the Predictive Maintenance
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From steel manufacturing point of view, it makes sense to use interlayer communication and IIoT computational power to feed core SCADA processes with intermediate and structured calculation results generated on fog layer rather than large quantity of raw measured data, reducing network latency.
“From the steel manufacturing process point of view, it makes sense to make advantage of interlayer communication i.e. direct communication between IIoT devices and their computational power to feed the core SCADA processes with intermediate and structured calculation results generated on fog layer [32] rather than a large quantity of raw measured data that negatively influence network performance,”
From SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0 (2022) · cites this paper as [32]
Citations
Cites
Research Directions for the Internet of Things
Stankovic, J. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2312291 · Cited by 1,941
Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges
Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2012.53 · Cited by 1,392
Industrie 4.0 in Produktion, Automatisierung und Logistik
Bauernhansl, T., ten Hompel, M., & Vogel‐Heuser, B. (2014) · Springer eBooks · doi:10.1007/978-3-658-04682-8 · Cited by 789
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2349359 · Cited by 763
A review of data mining applications for quality improvement in manufacturing industry
Köksal, G., Batmaz, İ., & Testik, M. C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.063 · Cited by 438
Smart Factory - A Step towards the Next Generation of Manufacturing
Lücke, D., Constantinescu, C., & Westkämper, E. (2008) · Springer eBooks · doi:10.1007/978-1-84800-267-8_23 · Cited by 376
The Maintenance Management Framework: Models and Methods for Complex Systems Maintenance
M√°rquez, A. C. (2007) · Springer eBooks · Cited by 249
Maintenance-related major accidents: Classification of causes and case study
Okoh, P., & Haugen, S. (2013) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2013.04.002 · Cited by 75
The control of the setting up of a predictive maintenance programme using a system of indicators
Carnero, M. C. (2003) · Omega · doi:10.1016/j.omega.2003.09.009 · Cited by 67
Support Vector Machines for indoor sensor localization
Farjow, W., Chehri, A., Mouftah, H., et al. (2011) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2011.5779231 · Cited by 36
Self-citationIndoor Cooperative Positioning Based on Fingerprinting and Support Vector Machines
Chehri, A., Mouftah, H., & Farjow, W. F. (2012) · Cited by 3
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Sverko, M., Grbac, T. G., & Mikuc, M. (2022) · IEEE Access · doi:10.1109/access.2022.3211288 · Cited by 71
“Another kind of IIoT application is implemented at the IT layer, linking together machine learning and data analytics tools that leverage on field IIoT devices, generating data in real time to make analysis accessible locally at the SCADA level as well as remotely via a cloud platform [32].”
BackgroundAutomatic Classification of Rotating Machinery Defects Using Machine Learning (ML) Algorithms
Zoungrana, W.-B., Chehri, A., & Zimmermann, A. (2021) · Smart Innovation, Systems and Technologies · doi:10.1007/978-981-15-5784-2_16 · Cited by 8
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Metadata
- Authors
- Chehri, A., & Jeon, G.
- Affiliations
- Université du Québec à Chicoutimi (CA)
- Incheon National University (KR)
- Corresponding author
- Chehri, Abdellah
- Venue
- Smart Innovation, Systems and Technologies
- Publisher
- Springer Singapore
- Published
- 2019
- Pages
- 517-527
- ISSN
- 2190-3018 (ISSN-L)
- 2190-3026
- OpenAlex
- W2948613845
- Cited by
- 42
- References
- 11
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 11 · 2022: 6 · 2021: 8 · 2020: 8 · 2019: 1
- FWCI
- 13.06
- Type
- book chapter
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Smart innovation, systems and technologies (publishedVersion)
- Constellation (Université du Québec à Chicoutimi) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Industrial Internet of Things, predictive maintenance, Industry 4.0, smart factory, cyber-physical systems, digital transformation, production downtime reduction, industrial machinery, production systems, process quality
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