A New Hierarchical Framework for Detection and Isolation of Multiple Faults in Complex Industrial Processes. Peng, K., Ren, Z., Dong, J., & Ma, L. IEEE Access, 7:12006–12015, 2019. Conference Name: IEEE Accessdoi abstract bibtex In actual production practice, the occurrence probability of multiple faults is much higher than that of a single fault. Since the composition of multiple faults is uncertain, it is difficult to establish a single model for multifault diagnosis. In this paper, a new hierarchical framework is proposed for solving multifault detection and isolation problems. First, an adaptive dynamic kernel independent component analysis method is proposed for time-varying and unknown multifault detection. After that, a sparse local exponential discriminant analysis method is developed for the multimodal multifault isolation problem. Finally, the Tennessee Eastman process is used to validate the performance of the proposed methods, and the experimental results show that the proposed methods can efficiently detect and isolate multiple faults.
@article{peng_new_2019,
title = {A {New} {Hierarchical} {Framework} for {Detection} and {Isolation} of {Multiple} {Faults} in {Complex} {Industrial} {Processes}},
volume = {7},
issn = {2169-3536},
doi = {10.1109/ACCESS.2019.2892487},
abstract = {In actual production practice, the occurrence probability of multiple faults is much higher than that of a single fault. Since the composition of multiple faults is uncertain, it is difficult to establish a single model for multifault diagnosis. In this paper, a new hierarchical framework is proposed for solving multifault detection and isolation problems. First, an adaptive dynamic kernel independent component analysis method is proposed for time-varying and unknown multifault detection. After that, a sparse local exponential discriminant analysis method is developed for the multimodal multifault isolation problem. Finally, the Tennessee Eastman process is used to validate the performance of the proposed methods, and the experimental results show that the proposed methods can efficiently detect and isolate multiple faults.},
journal = {IEEE Access},
author = {Peng, Kaixiang and Ren, Zhihao and Dong, Jie and Ma, Liang},
year = {2019},
note = {Conference Name: IEEE Access},
keywords = {Covariance matrices, Eigenvalues and eigenfunctions, Independent component analysis, Kernel, Monitoring, Multiple faults, Principal component analysis, Production, accurate isolation, complex industrial processes, hierarchical framework, real-time detection},
pages = {12006--12015},
}
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