The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis
Open accessAbstract
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos.
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Data-driven Soft Sensors in the process industry
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Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00161-8 · Cited by 1,530
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Cited by
Industry 4.0, digitization, and opportunities for sustainability
Ghobakhloo, M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119869 · Cited by 2,115
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440
Kamble, S., Gunasekaran, A., & Dhone, N. C. (2020) · International Journal of Production Research · doi:10.1080/00207543.2019.1630772 · Cited by 687
A Survey on Deep Learning for Data-Driven Soft Sensors
Sun, Q., & Ge, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3053128 · Cited by 665
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
Metadata
- Authors
- Reis, M., & Gins, G.
- Venue
- Processes
- Publisher
- MDPI AG
- Published
- 2017-06-30
- Volume
- 5
- Issue
- 3
- Pages
- 35
- ISSN
- 2227-9717 (ISSN-L)
- OpenAlex
- W2729884040
- Cited by
- 294
- References
- 74
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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