Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Early Detection of Discrepancies in Multichannel Monitoring Systems. / Макшанов, Андрей; Мусаев, Александр Азерович; Григорьев, Дмитрий Алексеевич.
Proceedings - 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 506-511.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Early Detection of Discrepancies in Multichannel Monitoring Systems
AU - Макшанов, Андрей
AU - Мусаев, Александр Азерович
AU - Григорьев, Дмитрий Алексеевич
PY - 2024/5/20
Y1 - 2024/5/20
N2 - This study addresses a comprehensive set of issues related to the construction and use of sequential algorithms for detecting spontaneous changes (discrepancies) in the probabilistic characteristics of multidimensional time series. The research is motivated by the challenges of mathematical support for decision-making processes based on multichannel monitoring of large systems and is dedicated to the analysis of the spatio-temporal dynamics of multidimensional time series measurements. As an alternative to traditional approaches, new technologies for anlyzing inter-channel connections are proposed. Dimensionality reduction technologies are used, based on the representation of data matrices in the first singular basis and multiple regression in the space of projections. The application of the developed approach is demonstrated in the task of analyzing the characteristics of turbulent flow based on pressure deviation measurements at various points in the volume.
AB - This study addresses a comprehensive set of issues related to the construction and use of sequential algorithms for detecting spontaneous changes (discrepancies) in the probabilistic characteristics of multidimensional time series. The research is motivated by the challenges of mathematical support for decision-making processes based on multichannel monitoring of large systems and is dedicated to the analysis of the spatio-temporal dynamics of multidimensional time series measurements. As an alternative to traditional approaches, new technologies for anlyzing inter-channel connections are proposed. Dimensionality reduction technologies are used, based on the representation of data matrices in the first singular basis and multiple regression in the space of projections. The application of the developed approach is demonstrated in the task of analyzing the characteristics of turbulent flow based on pressure deviation measurements at various points in the volume.
KW - MANOVA
KW - dimensionality reduction
KW - discrepancy detection
KW - multivariate statistical analysis
KW - singular decomposition
UR - https://www.mendeley.com/catalogue/6d3b76fd-1c03-3dc7-9593-9c0f0e4d7e54/
U2 - 10.1109/icieam60818.2024.10553662
DO - 10.1109/icieam60818.2024.10553662
M3 - Conference contribution
SN - 9798350395013
SP - 506
EP - 511
BT - Proceedings - 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2024 through 24 May 2024
ER -
ID: 123947504