[1] J X Hao, C Y Jia. Research on fault mode diagnosis of airborne circuit board based on infrared lmages. Infrared Technology, 41, 273-278(2019).

[2] [2] Li C L. Research on analog circuit fault diagnosis methods based on SVDD parameter identification[D]. Nanjing: Nanjing University of Aeronautics Astronautics, 2011. (in Chinese)

[3] G H Li, J Lei. Research on major technology of PCB infrared thermal detection based on information fusion. Infrared Technology, 39, 829-834(2017).

[4] [4] Nhisham M, Bugis I, Jamaludin I, et al. Thermal analysis on PCB using Galerkin approach[C]International Conference on Modeling. IEEE, 2011: 16

[5] J Varghese, T Singh, S Mohan. PCB thermal image analysis using MATLAB. International Journal of Recent Advances in Engineering & Technology, 2, 46-52(2014).

[6] M H Wang, Y K Chung, W T Sung. Using thermal image matter-element to design a circuit board fault diagnosis system. Expert Systems with Applications, 38, 6164-6169(2011).

[7] F Al-Obaidy, F Yazdani, F A Mohammadi. Fault detection using thermal image based on soft computing methods: Comparative study. Microelectronics Reliability, 71, 88-100(2017).

[8] [8] Cui W. Research on key technologies f printed circuit board detection by infrared thermgraphy[D]. Nanjing: Nanjing University of Aeronautics Astronautics, 2011. (in Chinese)

[9] L Wang, M Wang. Automatic image segmentation algorithm by PCNN based on mean threshold and Ostu. Infrared Technology, 37, 553-559(2015).

[10] K Wang, K Zhang, L Wang, et al. Infrared image segmentation based on MRF combined with two-algorithm game. Infrared Technology, 37, 134-138(2015).

[11] L Wang, P P Zeng, J X Hao. Infrared image registration of circuit board based on ROI gray compression. Laser & Infrared, 44, 313-318(2014).

[12] L Wang, S Li, H Wu, et al. Research of aviation circuit board fault diagnosis and prediction based on infrared technology. Computer Simulation, 34, 89-93(2017).

[13] J X Hao, C Y Jia. Circuit board fault diagnosis algorithm based SVM and improved D-S evidence theory. Modern Elec-tronics Technique, 42, 15-20(2019).

[14] L C Yann, Y Bengio, G Hinton. Deep learning. Nature, 147, 436-444(2015).

[15] J Schmidhuber. Deep learning in neural networks: An overview. Neural Newworks, 61, 85-117(2015).

[16] [16] Wang Z, Yan W, Oates T. Time series classification from scratch with deep neural wks: A strong baseline[C]International Joint Conference on Neural wks, 2017: 15781585.

[17] [17] Cui Z, Chen W, Chen Y. Multiscale convolutional neural wks f time series classification[EBOL]. (20160322)[20220714]. http:arxiv.gabs1603.06995.

[18] X W Zou, Z D Wang, Q Li, et al. Integration of residual network and convolutional neural network along with various activation functions and global pooling for time series classification. Neurocomputing, 367, 39-45(2019).

[19] F Karim, S Majumdar, H Darabi, et al. LSTM fully con-volutional networks for time series classification. IEEE Access, 6, 1662-1669(2018).

[20] [20] Elsayed N, Maida A S, Bayoumi M. Gated recurrent neural wks empirical utilization f time series classification [C] 2019 International Conference on Inter of Things (iThings) IEEE Green Computing Communications (GreenCom) IEEE Cyber, Physical Social Computing (CPSCom) IEEE Smart Data (SmartData), 2019: 12071210.

[21] [21] Hochreiter S, Schhuber J A . LSTM can solve hard long time lag problems [C]Neural Infmation Processing Systems, MIT Press, 1996: 473479.

[22] [22] Chowski J, Bahdanau D, Serdyuk D, et al. AttentionBased Models f Speech Recognition[C]NIPS''15: Proceedings of the 28th International Conference on Neural Infmation Processing Systems, 2015, 1: 577585.

[23] G Xie, A Shangguan, R Fei, et al. Motion trajectory prediction based on a CNN-LSTM sequential model. Science China Information Sciences, 63, 233-253(2020).

[24] Liang Zhao, Chunyang Mo, Jiajun Ma, et al. LSTM-MFCN: A time series classifier based on multi-scale spatial-temporal features. Computer Communications, 182, 52-59(2022).

[25] [25] Tang W, Long G, Liu L, et al. Rethinking 1DCNN f time series classification: A stronger baseline[C]The Tenth International Conference on Learning Representations(ICLR 2022), 2022.

[26] F Karim, S Majumdar, H Darabi, et al. Multivariate LSTM-FCNs for time series classification. Neural Networks, 116, 237-245(2018).

[27] Z Xiao, X Xu, H Xing, et al. RTFN: A robust temporal feature network for time series classification. Information Sciences, 571, 65-86(2021).

[28] [28] Zhu Y, Zhao C, Guo H, et al. Attention couple: Fully convolutional attention coupling wk f object detection [C]IEEE Transactions on Image Processing, 2019: 113126.