The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning
Open accessAbstract
Abstract As IoT-enabled manufacturing is still in its infancy, there are several key research gaps that need to be addressed. These gaps include the understanding of the characteristics of the big data generated from industrial IoT sensors, the challenges they present to process data analytics, as well as the specific opportunities that the IoT big data could bring to advance manufacturing. In this paper, we use an inhouse-developed IoT-enabled manufacturing testbed to study the characteristics of the big data generated from the testbed. Since the quality of the data usually has the most impact on process modeling, data veracity is often the most challenging characteristic of big data. To address that, we explore the role of feature engineering in developing effective machine learning models for predicting key process variables. We compare complex deep learning approaches to a simple statistical learning approach, with different level or extent of feature engineering, to explore their pros and cons for potential industrial IoT-enabled manufacturing applications.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The most mentioned reasons are heterogeneous multiple data sources [4,12,30,44,50], sensor malfunctions [15,31,35], network problems (connection failures, communication delays) [16,37,40], high sampling frequency [18], and cyber attacks for data tampering [7,31].”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [37]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Least-squares frequency analysis of unequally spaced data
Lomb, N. R. (1976) · Astrophysics and Space Science · doi:10.1007/bf00648343 · Cited by 5,717
(2011) · Cited by 5,203
(2011) · Cited by 3,939
A review of variable selection methods in Partial Least Squares Regression
Mehmood, T., Liland, K. H., Snipen, L., & Sæbø, S. (2012) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2012.07.010 · Cited by 1,591
The promise of artificial intelligence in chemical engineering: Is it here, finally?
Venkatasubramanian, V. (2019) · AIChE Journal · doi:10.1002/aic.16489 · Cited by 664
(2009) · Elsevier eBooks · doi:10.1016/c2009-0-28356-5 · Cited by 423
Research works printed but not linked: 1
- Wind, 2014, Concepts in predictive machine learning, Tech. Univ. Denmark, 1
Cited by
Raja Santhi, A., & Muthuswamy, P. (2023) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-023-01217-8 · Cited by 353
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
Melo, A., Câmara, M. M., & Pinto, J. C. (2024) · Processes · doi:10.3390/pr12020251 · Cited by 122
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 84
Gupta, P., Krishna, C., Rajesh, R., Ananthakrishnan, A., Vishnuvardhan, A., Patel, S. S., Kapruan, C., Brahmbhatt, S., Kataray, T., Narayanan, D., et al. (2022) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-022-01075-w · Cited by 79
RetractedCybersecurity in process control, operations, and supply chain
Parker, S., Wu, Z., & Christofides, P. D. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108169 · Cited by 72
Metadata
- Authors
- Shah, D., Wang, J., & He, Q. P.
- Corresponding author
- Wang, Jin
- He, Q. Peter
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 141
- Pages
- 106970
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W3034202893
- Cited by
- 69
- References
- 37
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 10 · 2023: 16 · 2022: 15 · 2021: 11 · 2020: 3
- FWCI
- 6.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 266 d)
- Locations
- Computers & Chemical Engineering (acceptedVersion)
- Funders
- National Science Foundation (CBET #1805950)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- feature engineering, big data analytics, Industrial Internet of Things, smart manufacturing, data veracity, data quality, deep learning, statistical learning, process parameter prediction, classifier comparison
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: Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning. https://app.cochat.ai/papers/10.1016/j.compchemeng.2020.106970
- The paper
- Shah, D., Wang, J., & He, Q. P. (2020). Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2020.106970
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.
