The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Total projection to latent structures for process monitoring
Abstract
Partial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component analysis. Standard PLS often requires many components or latent variables (LVs), which contain variations orthogonal to Y and useless for predicting Y. Further, the X‐residual of PLS usually has quite large variations, thus is not proper to monitor with the Q‐statistic. To reduce false alarm and missing alarm rates of faults related to Y, a total projection to latent structures (T‐PLS) algorithm is proposed in this article. The new structure divides the X‐space into four parts instead of two parts in standard PLS. The properties of T‐PLS are studied in detail, including its relationship to the orthogonal PLS. Further study shows the space decomposition on X‐space induced by T‐PLS. Fault detection policy is developed based on the T‐PLS. Case studies on two simulation examples show the effectiveness of the T‐PLS based fault detection methods. © 2009 American Institute of Chemical Engineers AIChE J, 2010
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Cites
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905 · cited 1×
“As rank(Tortho) 1⁄4 rank(To) 1⁄4 A 1, if (19) does not hold, there must exist A linearly independent columns in [To, Tortho], so that span{Tortho, To} 1⁄4 span{T}.”
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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
“[158] proposed a total projection form of the PLS model for the purpose of process monitoring.”
BackgroundImproved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
Yin, S., Zhu, X., & Kaynak, O. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2345331 · Cited by 537
Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures
Yin, S., Wang, G., & Gao, H. (2016) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2015.2481318 · Cited by 258
Metadata
- Authors
- Zhou, D., Li, G., & Qin, S. J.
- Venue
- AIChE Journal
- Publisher
- Wiley
- Published
- 2009-08-26
- Volume
- 56
- Issue
- 1
- Pages
- 168-178
- ISSN
- 0001-1541 (ISSN-L)
- 1547-5905
- OpenAlex
- W2103711750
- Cited by
- 472
- References
- 1
- FWCI
- 12.82
- Type
- journal article
- Language
- en
- Open access
- closed
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