The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures
Abstract
Quality- or output-related fault detection has attracted much attention in recent years. Several approaches have been developed to solve this issue based on postprocessing schemes. However, further studies find that these methods gradually lose their functions when amplitudes of quality-unrelated faults increase; in addition, they still consume a relatively large amount of calculation load in practice. In this brief, we propose a new structure of preprocessing-modeling-postprocessing, within which modified orthogonal projections to latent structures (MOPLS) method is developed. Compared with the previous approaches, the new method significantly improves the performance of quality-related fault detection. In addition, it reduces the number of required latent variables, thus it has a quite lower computational load than the previous ones. A numerical example and the Tennessee Eastman process are used to verify the effectiveness of the proposed approach.
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Citations
Cites
Orthogonal projections to latent structures (O‐PLS)
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Self-citationMultivariate Control Charts for Individual Observations
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Geometric properties of partial least squares for process monitoring
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Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods
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Cited by
A new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222
Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633
A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles
Kommuri, S. K., Defoort, M., Karimi, H. R., & Veluvolu, K. C. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2590993 · Cited by 408
Hu, X., Cao, D., & Egardt, B. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2675920 · Cited by 205
Metadata
- Authors
- Yin, S., Wang, G., & Gao, H.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2016-07
- Volume
- 24
- Issue
- 4
- Pages
- 1480-1487
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2413579316
- Cited by
- 258
- References
- 14
- FWCI
- 47.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yin, S., Wang, G., & Gao, H. (2016). Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2015.2481318
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