Infrared Technology, Volume. 44, Issue 8, 870(2022)

Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments

Zhihong HUANG1、*, Feng HONG2, and Wei HUANG1,3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    References(5)

    [13] [13] YUAN X, YANG J. Sparse and low-rank matrix decomposition via alternating direction methods[J]. Pacific. J. Optim, 1990, 9(1): 1760-1770.

    [14] [14] Reed I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustic Speech Signal Processing, 1990, 38(10): 1760-1770.

    [15] [15] KANG X, ZHANG X, LI S, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(10): 5600-5611.

    [16] [16] XU Y, WU Z, LI J, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Trans. Geosci Remote Sens., 2016, 54(4): 1990

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    HUANG Zhihong, HONG Feng, HUANG Wei. Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments[J]. Infrared Technology, 2022, 44(8): 870

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    Paper Information

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    Received: Feb. 10, 2022

    Accepted: --

    Published Online: Oct. 16, 2022

    The Author Email: Zhihong HUANG (zhihong_huang111@163.com)

    DOI:

    CSTR:32186.14.

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