Journal of System Simulation
Abstract
Abstract: Aiming at the small faults and the common data non-linear problems of industrial process, a fault detection method based oncumulative sum of neighbor statistic (CUSUM-NS) is proposed. Mutual information principal component analysis (MIPCA) is used to reduce the dimension of training data, and the principal components based on mutual information are extracted to construct a new sample space. For the new sample space after dimensionality reduction, the nonlinear features of the process data can be fully extracted through the distance square sum statistics of k nearest neighbors. Cumulative summation(CUSUM) method is used to accumulate the sum of squares of neighboring distances to capture the small changes in process data. The simulation is conducted through the Tennessee Eastman (TE) process, and the results verify the effectiveness of the proposed method.
Recommended Citation
Guo, Xiaoping; Gao, Jiajun; Guo, Jianbin; and Yuan, Li
(2021)
"Small Fault Detection Based on Cumulative Sum of Neighbor Statistic,"
Journal of System Simulation: Vol. 33:
Iss.
4, Article 5.
DOI: 10.16182/j.issn1004731x.joss.19-0663
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss4/5
First Page
792
Revised Date
2020-05-12
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0663
Last Page
800
CLC
TP277
Recommended Citation
Guo Xiaoping, Gao Jiajun, Guo Jianbin, Li Yuan. Small Fault Detection Based on Cumulative Sum of Neighbor Statistic[J]. Journal of System Simulation, 2021, 33(4): 792-800.
DOI
10.16182/j.issn1004731x.joss.19-0663
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons