References
Zhou D H, Li G, Li Y. Data-driven Fault Diagnosis Technology for Industrial Processes. Beijing: Science and Technology Press, 2011
Gueddi I, Nasri O, Benothman K, et al. 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
Wang H J, Zuo Y B. Spindle fault diagnosis method based on locally linear dimension reduction topological space (in Chinese). J Beijing Univ Inform Sci Technol, 2014, 29: 55–58
Song B, Zhou X G, Shi H B, et al. Performance-indicator-oriented concurrent subspace process monitoring method. IEEE Trans Ind Electron, 2019, 66: 5535–5545
Deng T Q, Liu J Y, Wang N. Locally linear embedding method for high dimensional data outlier detection (in Chinese). Comput Eng Appl, 2018, 54: 115–122
Zou W D, Xia Y Q, Li H F. Fault diagnosis of tennessee-eastman process using orthogonal incremental extreme learning machine based on driving amount. IEEE Trans Cybern, 2018, 48: 3403–3410
Bo C M, Han X C, Yi H, et al. LLE algorithm and fault detection based on clustering selection k-nearest neighbor (in Chinese). J Chem Eng, 2016, 67: 925–930
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61490701, 61673279).
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Li, Y., Feng, C. Industrial process fault detection based on locally linear embedded latent mapping. Sci. China Inf. Sci. 65, 149201 (2022). https://doi.org/10.1007/s11432-019-2896-x
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DOI: https://doi.org/10.1007/s11432-019-2896-x