The DOI matches its Crossref record.
Statistical process monitoring as a big data analytics tool for smart manufacturing
Abstract
Abstract With ever-accelerating advancement of information, communication, sensing and characterization technologies, such as industrial Internet of Things (IoT) and high-throughput instruments, it is expected that the data generated from manufacturing will grow exponentially, generating so called ‘big data’. One of the focuses of smart manufacturing is to create manufacturing intelligence from real-time data to support accurate and timely decision-making. Therefore, big data analytics is expected to contribute significantly to the advancement of smart manufacturing. In this work, a roadmap of statistical process monitoring (SPM) is presented. Most recent developments in SPM are briefly reviewed and summarized. Specific challenges and potential solutions in handling manufacturing big data are discussed. We suggest that process characteristics or feature based SPM, instead of process variable based SPM, is a promising route for next generation SPM and could play a significant role in smart manufacturing. The advantages of feature based SPM are discussed to support the suggestion and future directions in SPM are discussed in the context of smart manufacturing.
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
Cited by
A review of industrial big data for decision making in intelligent manufacturing
Li, C., Chen, Y., & Shang, Y. (2022) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2021.06.001 · Cited by 325
Liu, X., Qian, C., Hatcher, W. G., Xu, H., Liao, W., & Yu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2920763 · Cited by 152
Shah, D., Wang, J., & He, Q. P. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106970 · Cited by 69
Self-citation
Metadata
- Authors
- He, Q. P., & Wang, J.
- Venue
- Journal of Process Control
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 67
- Pages
- 35-43
- ISSN
- 0959-1524 (ISSN-L)
- OpenAlex
- W2734575787
- Cited by
- 201
- References
- 0
- FWCI
- 13.95
- 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: Statistical process monitoring as a big data analytics tool for smart manufacturing. https://app.cochat.ai/papers/10.1016/j.jprocont.2017.06.012
- The paper
- He, Q. P., & Wang, J. (2018). Statistical process monitoring as a big data analytics tool for smart manufacturing. Journal of Process Control. https://doi.org/10.1016/j.jprocont.2017.06.012
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.
