The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Review of Recent Research on Data-Based Process Monitoring
Abstract
Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Cited by
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922
Self-citationReview on data-driven modeling and monitoring for plant-wide industrial processes
Ge, Z. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.021 · Cited by 630
Self-citationAdvances and opportunities in machine learning for process data analytics
Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351
A deep belief network based fault diagnosis model for complex chemical processes
Zhang, Z., & Zhao, J. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.02.041 · Cited by 309
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
Self-citation
Metadata
- Authors
- Ge, Z., Song, Z., & Gao, F.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-03-01
- Volume
- 52
- Issue
- 10
- Pages
- 3543-3562
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2322097696
- Cited by
- 967
- References
- 2
- FWCI
- 87.68
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Review of Recent Research on Data-Based Process Monitoring. https://app.cochat.ai/papers/10.1021/ie302069q
- The paper
- Ge, Z., Song, Z., & Gao, F. (2013). Review of Recent Research on Data-Based Process Monitoring. Industrial & Engineering Chemistry Research. https://doi.org/10.1021/ie302069q
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
