Infrared and Laser Engineering, Volume. 52, Issue 4, 20220492(2023)

Research on circuit board fault diagnosis based on infrared temperature series

Jianxin Hao1 and Li Wang2
Author Affiliations
  • 1Engineering Techniques Training Center, Civil Aviation University of China, Tianjin 300300, China
  • 2Vocational and Technical College, Civil Aviation University of China, Tianjin 300300, China
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    Figures & Tables(14)
    Structure of circuit board fault diagnosis model
    Structure diagram of dilated Conv1D block
    Structure of LSTMwAtt
    Infrared image of power module
    Temperature change curves of each chip when U1 fails
    Structure of self-made temperature series datasets
    Results of training and validation in Datasets_1 and Datasets_2
    Comparison of diagnostic results of different algorithm models
    • Table 1. Failure mode description

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      Table 1. Failure mode description

      ModeDescriptionModeDescriptionModeDescriptionModeDescription
      F1NormalF8pin2 of U2-open F15pin3 of U3-open F22pin2 and 3 of U2 chip-short
      F2pin2 of U1-open F9pin3 of U2-openF16pin4 of U3-open F23pin3 and 4 of U2 chip-short
      F3pin3 of U1-open F10pin4 of U2-open F17pin6 of U3-open F24pin6 and 7 of U2 chip-short
      F4pin4 of U1-open F11pin5 of U2-open F18pin7 of U3-open F25pin2 and 3 of U3 chip-short
      F5pin6 of U1-open F12pin6 of U2-open F19pin2 and 3 of U1 chip-short F26pin3 and 4 of U3 chip-short
      F6pin7 of U1-open F13pin7 of U2-open F20pin3 and 4 of U1 chip-short F27pin6 and 7 of U3 chip-short
      F7pin1 of U1-open F14pin1 of U3-open F21pin6 and 7 of U1 chip-short
    • Table 2. Parameters of self-made temperature series datasets

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      Table 2. Parameters of self-made temperature series datasets

      SamplesSequenceFeaturesPurpose
      Datasets_1195481203Train & Verify
      Datasets_2195481206Train & Verify
      Datasets_348871203Test
      Datasets_448871206Test
    • Table 3. Hyperparameters settings of FEN

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      Table 3. Hyperparameters settings of FEN

      ParametersDCB:Layer_1DCB:Layer_2DCB:Layer_3CB
      Dilated_1Dilated_2Dilated_3Dilated_1Dilated_2Dilated_3Conv1DBlock1Block2Block3
      Filters128256128128
      Dilation rate1241241-
      ActivationLeakRelu
      Filter size853751
      Receptive field1×82×84×81×52×54×51×3751
      Scales proportion112112--
      121121
      211211
    • Table 4. Performance of different kernel quantity

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      Table 4. Performance of different kernel quantity

      1∶1∶21∶2∶12∶1∶1
      Datasets_192.39%95.15%92.18%
      Datasets_298.19%98.98%97.07%
    • Table 5. Performance comparison of different classi-fication algorithm models

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      Table 5. Performance comparison of different classi-fication algorithm models

      FCNMFCNLSTMLSTM-FCNProposed
      Datasets_193.21%94.71%91.78%94.83%95.15%
      Datasets_297.12%96.69%96.35%98.44%98.98%
      Datasets_380.71%83.30%80.09%85.80%91.15%
      Datasets_488.8%89.56%85.58%91.71%96.27%
    • Table 6. Performance comparison of ablation experiment

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      Table 6. Performance comparison of ablation experiment

      ReluNo_DilateNo_SelfAttAttLSTM1_LSTM3_LSTM
      Datasets_389.73%90.23%90.21%89.37%88.54%83.38%
      Datasets_495.75%95.67%95.07%95.97%93.82%90.80%
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    Jianxin Hao, Li Wang. Research on circuit board fault diagnosis based on infrared temperature series[J]. Infrared and Laser Engineering, 2023, 52(4): 20220492

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    Paper Information

    Category: Infrared technology and application

    Received: Jul. 14, 2022

    Accepted: --

    Published Online: Jul. 4, 2023

    The Author Email:

    DOI:10.3788/IRLA20220492

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