SCIENCE CHINA Information Sciences, Volume 65 , Issue 4 : 149201(2022) https://doi.org/10.1007/s11432-019-2896-x

Industrial process fault detection based on locally linear embedded latent mapping

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  • ReceivedNov 3, 2019
  • AcceptedApr 1, 2020
  • PublishedMar 19, 2021


There is no abstract available for this article.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61490701, 61673279).


[1] Zhou D H, Li G, Li Y. Data-driven Fault Diagnosis Technology for Industrial Processes. Beijing: Science and Technology Press, 2011. Google Scholar

[2] Gueddi I, Nasri O, Benothman K. Fault Detection and Isolation of spacecraft thrusters using an extended principal component analysis to interval data. Int J Control Autom Syst, 2017, 15: 776-789 CrossRef Google Scholar

[3] Wang H J, Zuo Y B. Principal axis fault diagnosis method based on locally linear reduced dimension topological space (in Chinese). J Beijing Univ Inform Sci Technol, 2014, 29: 55--58. Google Scholar

[4] Song B, Zhou X, Shi H. Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method. IEEE Trans Ind Electron, 2019, 66: 5535-5545 CrossRef Google Scholar

[5] Deng T Q, Liu J Y, Wang N. Local linear embedding method for outlier detection of high-dimensional data (in Chinese). Comput Eng Appl, 2018, 54: 115--122. Google Scholar

[6] Zou W, Xia Y, Li H. Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount. IEEE Trans Cybern, 2018, 48: 3403-3410 CrossRef Google Scholar

[7] Bo C M, Han X C, Yi H, et al. CLE algorithm and fault detection based on clustering k-nearest neighbors (in Chinese). J Chem Eng, 2016, 67: 925--930. Google Scholar