DataVXNondestructive Testing and EvaluationPaper
journal article·2026-05-14

CSSCL: a cross-sensor self-contrastive learning for nondestructive fault detection of heavy locomotive motors in label-scarce scenarios

Yuan Xu, Deqiang He, Xianguang Luo, Zhenzhen Jin, Haimeng Sun

Nondestructive Testing and Evaluation
Publisher page
Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Machine Fault Diagnosis Techniques · Machine Learning and ELM · VLSI and Analog Circuit Testing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical Engineering of Guangxi University

State Key Laboratory of Heavy-Duty and Express High-Power Electric Locomotive

State Owned Daguishan Forest Farm in Guangxi Zhuang Autonomous Region

Funding

Guangxi Natural Science Foundation · Guangxi Science and Technology Program · the Major Science and Technology Project of Guangxi Province of China · the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive Fund · Innovation Project of Guangxi Graduate Education and Major Talent Project in Guangxi Zhuang Autonomous Region

References (41)

Show source-provided references
1. 10.1007/s11356-022-24150-x10.1007/s11356-022-24150-x ↗
2. 10.1016/j.measurement.2022.11075910.1016/j.measurement.2022.110759 ↗
3. 10.1080/10589759.2025.252790410.1080/10589759.2025.2527904 ↗
4. 10.1080/10589759.2024.241369610.1080/10589759.2024.2413696 ↗
5. 10.1016/j.ndteint.2023.10288810.1016/j.ndteint.2023.102888 ↗
6. 10.1016/j.ndteint.2019.10213810.1016/j.ndteint.2019.102138 ↗
7. 10.1016/j.ijepes.2022.10821910.1016/j.ijepes.2022.108219 ↗
8. 10.1080/10589759.2025.254833810.1080/10589759.2025.2548338 ↗
9. 10.1016/j.apacoust.2025.11056310.1016/j.apacoust.2025.110563 ↗
10. 10.1177/1475921726144079810.1177/14759217261440798 ↗
11. 10.1016/j.measurement.2026.12129110.1016/j.measurement.2026.121291 ↗
12. 10.1177/1475921725140558410.1177/14759217251405584 ↗
13. 10.1109/TCE.2025.363195410.1109/TCE.2025.3631954 ↗
14. 10.1016/j.knosys.2023.11120510.1016/j.knosys.2023.111205 ↗
15. 10.1016/j.ress.2024.10998010.1016/j.ress.2024.109980 ↗
16. 10.1080/10589759.2024.244197810.1080/10589759.2024.2441978 ↗
17. 10.1109/TMECH.2019.292896710.1109/TMECH.2019.2928967 ↗
18. 10.1109/TPEL.2024.343216310.1109/TPEL.2024.3432163 ↗
19. 10.1016/j.inffus.2024.10262110.1016/j.inffus.2024.102621 ↗
20. 10.1016/j.renene.2024.12196510.1016/j.renene.2024.121965 ↗
21. 10.1109/TIM.2026.365754910.1109/TIM.2026.3657549 ↗
22. 10.1016/j.ress.2023.10915210.1016/j.ress.2023.109152 ↗
23. 10.1109/TIM.2020.298741310.1109/TIM.2020.2987413 ↗
24. 10.1109/TIM.2023.328266410.1109/TIM.2023.3282664 ↗
25. 10.1016/j.eswa.2023.12158510.1016/j.eswa.2023.121585 ↗
26. 10.1016/j.knosys.2022.10846610.1016/j.knosys.2022.108466 ↗
27. 10.1109/ACCESS.2021.304919310.1109/ACCESS.2021.3049193 ↗
28. 10.1088/1361-6501/ad068e10.1088/1361-6501/ad068e ↗
29. 10.1109/TIM.2025.354469810.1109/TIM.2025.3544698 ↗
30. 10.1109/TIM.2025.356073310.1109/TIM.2025.3560733 ↗
31. 10.1016/j.knosys.2025.11307510.1016/j.knosys.2025.113075 ↗
32. 10.1109/TNNLS.2025.358285810.1109/TNNLS.2025.3582858 ↗
33. 10.1109/TASE.2023.326564910.1109/TASE.2023.3265649 ↗
34. 10.1109/TIM.2025.352761210.1109/TIM.2025.3527612 ↗
35. 10.1016/j.measurement.2021.10953210.1016/j.measurement.2021.109532 ↗
36. 10.1016/j.ymssp.2024.11226210.1016/j.ymssp.2024.112262 ↗
37. 10.1016/j.conengprac.2024.10590010.1016/j.conengprac.2024.105900 ↗
38. 10.1007/s11071-025-11101-710.1007/s11071-025-11101-7 ↗
39. 10.1016/j.eswa.2025.12694710.1016/j.eswa.2025.126947 ↗
40. 10.1016/j.knosys.2023.11063410.1016/j.knosys.2023.110634 ↗
41. Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579–2605.