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Journal of System Simulation

Abstract

Abstract: Modern industrial production equipment usually has a complex structure and runs alternately in different working conditions. Accurate working conditions identification based on monitoring data is the basis of health monitoring of the system, but the monitoring data of the system usually has a high dimension and a large data volume. To identify the complex equipment operating conditions, an unsupervised operating condition identification method based on stochastic neighborhood embedding is proposed. The stochastic neighborhood embedding algorithm can simultaneously preserve the local and global structural characteristics of the data, and also calculate the probability similarity of data points in high-dimensional and low-dimensional space to achieve the dimensionality reduction and unsupervised clustering of equipment high-dimensional monitoring data and to accurately identify the system operating conditions without establishing a system model. The results show that the proposed method can effectively identify complex operating conditions from high-dimensional monitoring data, which is an effective unsupervised clustering learning method.

First Page

1334

Last Page

1343

CLC

TP391.9

Recommended Citation

Huang Lin, Liu Shanjun, Wang Wei, et al. Unsupervised Complex Condition Recognition Based on Stochastic Neighborhood Embedding[J]. Journal of System Simulation, 2024, 36(6): 1334-1343.

Corresponding Author

Gong Li

DOI

10.16182/j.issn1004731x.joss.23-0336

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