The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Big data analytics for intelligent manufacturing systems: A review
Abstract
Abstract With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.
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
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
One Pixel Attack for Fooling Deep Neural Networks
Su, J., Vargas, D. V., & Sakurai, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2890858 · Cited by 2,406
Apache Hadoop YARN: yet another resource negotiator
Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al. (2013) · Proceedings of the 4th annual Symposium on Cloud Computing · doi:10.1145/2523616.2523633 · Cited by 1,848
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.03.014 · Cited by 1,545
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.12.051 · Cited by 879
Cited by
On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022) · Applied Sciences · doi:10.3390/app12168081 · Cited by 528
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152
“…enables a flexible and convenient way of transmitting data from local devices for machine learning applications, such as intelligent transport [59], [60], [61], intelligent marketing [62], intelligent manufacturing [63], smart home [64], intelligent security [65], and intelligent health [66].”
BackgroundArtificial intelligence techniques for implementation of intelligent machining
Hashmi, A. W., Mali, H. S., Meena, A., Khilji, I. A., Hashmi, M. F., & Saffe, S. N. B. M. (2022) · Materials Today: Proceedings · doi:10.1016/j.matpr.2021.11.277 · Cited by 24
Big Data Life Cycle in Shop-Floor–Trends and Challenges
Pulikottil, T., Estrada-Jimenez, L. A., Abadía, J. J. P., Carrera-Rivera, A., Torayev, A., Rehman, H. U., Mo, F., Nikghadam-Hojjati, S., & Barata, J. (2023) · IEEE Access · doi:10.1109/access.2023.3253286 · Cited by 14
Metadata
- Authors
- Wang, J., Xu, C., Zhang, J., & Zhong, R.
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 62
- Pages
- 738-752
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3165938138
- Cited by
- 603
- References
- 16
- FWCI
- 43.37
- 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: Big data analytics for intelligent manufacturing systems: A review. https://app.cochat.ai/papers/10.1016/j.jmsy.2021.03.005
- The paper
- Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2021.03.005
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.
