Neuromorphic Computing of Multimodal Temporal Data for Machine Fault Diagnosis: Methodology and Hardware Deployment

Weipeng Fan; Xiang Li; Yaguo Lei; Shupeng Yu; Naipeng Li; Bin Yang

doi:10.1109/JAS.2025.125813

Volume 13 Issue 6

Jun. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
Google Scholar h5-index: 95， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(6): 1515-1517

W. Fan, X. Li, Y. Lei, S. Yu, N. Li, and B. Yang, “Neuromorphic computing of multimodal temporal data for machine fault diagnosis: Methodology and hardware deployment,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1515–1517, Jun. 2026. doi: 10.1109/JAS.2025.125813

Citation:

W. Fan, X. Li, Y. Lei, S. Yu, N. Li, and B. Yang, “Neuromorphic computing of multimodal temporal data for machine fault diagnosis: Methodology and hardware deployment,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1515–1517, Jun. 2026. doi: 10.1109/JAS.2025.125813

Citation:

PDF( 1563 KB)

Neuromorphic Computing of Multimodal Temporal Data for Machine Fault Diagnosis: Methodology and Hardware Deployment

doi: 10.1109/JAS.2025.125813

More Information

Abstract

FullText(HTML)

References(10)

References

[1]	B. Yang, Y. Lei, X. Li, N. Li, and A. K. Nandi, “Label recovery and trajectory designable network for transfer fault diagnosis of machines with incorrect annotation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 932–945, 2024.
[2]	X. Gu, Y. Shang, Y. Kang, J. Li, Z. Mao, and C. Zhang, “An early minor-fault diagnosis method for lithium-ion battery packs based on unsupervised learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 810–812, 2023.
[3]	J. Huang, Z. Li, and Z. Zhou, “A simple framework to generalized zero-shot learning for fault diagnosis of industrial processes,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1504–1506, 2023.
[4]	A. Mehonic and A. J. Kenyon. “Brain-inspired computing needs a master plan”, Nature, vol. 604, pp. 255–260, 2022.
[5]	X. Chen, X. Li, S. Yu, Y. Lei, N. Li, and B. Yang, “Dynamic vision enabled contactless cross-domain machine fault diagnosis with neuromorphic computing,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 788–790, 2024.
[6]	Z. Xu, Y. Ma, Z. Pan, and X. Zheng, “Deep spiking residual shrinkage network for bearing fault diagnosis,” IEEE Trans. Cybern., vol. 54, no. 3, pp. 1608–1613, 2024.
[7]	L. Xu and D. Ji, “Online fault diagnosis using bioinspired spike neural network,” IEEE Trans. Industrial Informatics, vol. 20, no. 9, pp. 11466–11474, 2024.
[8]	P. Bonazzi, S. Bian, G. Lippolis, Y. Li, S. Sheik, and M. Magno, “Retina: Low-power eye tracking with event camera and spiking hardware,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2024, pp. 5684–5692.
[9]	S. B. Shrestha and G. Orchard, “Slayer: Spike layer error reassignment in time,” Advances in Neural Information Processing Systems, vol. 31, pp. 1412–1421, 2018.
[10]	M. Yao, O. Richter, G. Zhao, et al., “Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip”, Nature Commun., vol. 45, no. 1, Art. no. 4464, 2024.