Skip to main content
Springer Nature Link
Log in

Micro vibration detection algorithm for rotating machinery based on visual target detection

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Timely and effective acquisition of micro-vibrations of rotating machinery can diagnose the fault status of the equipment. Visual target detection schemes have the advantages of contactless, real-time and high accuracy. However, detecting micro-amplitude vibration of high-speed rotating machinery requires high accuracy. Therefore, in this paper, a high-speed camera is used as an acquisition medium to collect end-face vibration information from a rotor vibration test bench, and a target detection model YOLO-MVD is proposed to recognize micro-vibrations of rotating machinery to accurately detect rotor displacements. Firstly, we used EfficientViT backbone to replace YOLOv8n’s backbone, and secondly, we introduced Ghost Net’s lightweight thinking to deal with the C2F module, and then embedded in the neck network CBAM attention mechanism to improve the network feature extraction ability, and finally the WIoU v3 loss function to improve network performance. The algorithm is trained on the self-constructed rotor end-face image dataset, comparing multiple visual detection algorithms all show excellent performance, the accuracy rate reached 95.78%, the recall rate reached 90.34%, the average precision mean value reached 95.63%, and the performance is outstanding in terms of lightweighting and detection speed, with the model size compressed to 2.94 m and the frame rate increased to 114 FPS. and the performance is outstanding in comparing the laser Doppler vibrometer validation experiments in the frequency domain peak accuracy error range of 0.001. The research results verify the feasibility of the visual inspection program for rotating machinery, and provide a reference program for research in the field of practical industrial vibration detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Gradzki, R., Kulesza, Z., Bartoszewicz, B.: Method of shaft crack detection based on squared gain of vibration amplitude. Nonlinear Dyn. 98(1), 671–690 (2019)

    MATH  Google Scholar 

  2. Hadj, S.L., Lousdad, A., Bouamama, M., et al.: Vibration-based fault diagnosis of dynamic rotating systems for real-time maintenance monitoring. Int. J. Adv. Manuf. Technol. 126(7–8), 3283–3296 (2023)

    MATH  Google Scholar 

  3. Xiao, H., Daohong, W., Dong, L., et al.: Fault Diagnosis for Rotor Based on multi-sensor Information and Progressive Strategies. Measurement Science and Technology, 34 – 6 (2023)

    MATH  Google Scholar 

  4. Wisal, M., Oh, Y.K.: A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft. Sensors, 23 – 16 (2023)

    MATH  Google Scholar 

  5. Yanda, S., Ling, L., Jun, L., et al.: Computer vision based target-free 3D vibration displacement measurement of structures. Eng. Struct. 246 (2021)

  6. Mingfeng, H., Baiyan, Z., Wenjuan, L., et al.: A deep learning augmented vision-based method for measuring dynamic displacements of structures in harsh environments. J. Wind Eng. Ind. Aerodyn. 217 (2021)

  7. Cong, P., ZhaoZhou, C., BingYun, Y.: Full-field visual vibration measurement of rotating machine under complex conditions via unsupervised retinex model. IEEE Sens. J. 23(4), 3815–3824 (2023)

  8. Cong, P., Zeng, C., YanGang, W.: Phase-based noncontact vibration measurement of high-speed magnetically suspended rotor. IEEE Trans. Instrum. Meas. 69(7), 4807–4817 (2020)

  9. RongLiang, Y., Sen, W., Xing, W., et al.: Vision tracking multi-rotor displacement measurement. J. Vib. Eng. 37(1), 113–125 (2024)

  10. Huo, L., JianLin, M., HongJun, S., et al.: A new benchmark for vibration displacement detection of rotor. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)

  11. Redmon, J., Divvala, S., Girshick, R., et al.: You Only Look Once: Unified, Real-Time Object Detection. IEEE Computer Society. Los Alamitos, CA, USA, pp 779–788 (2016)

  12. EDMON, J.: FARHADI, A.: YOLOv3: An incremental improvement. Comput. Sci.1804.02767 (2018)

  13. WANG, C.,, Y., BOCHKOVSKIY,, A., LIAO,, H.: YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Conf. Comput. Vis. Pattern Recognit. (CVPR). IEEE, 7464–7475 (2023)

  14. Chatterton, S., Dassi, L., Gheller, E., et al.: Torsional Vibration Analysis Using Rotational Laser Vibrometers. Sensors, 24 – 6 (2024)

    Google Scholar 

  15. Ultralytics:YOLOv5 release v6.1: (2022). https://github.com/ultralytics/yolov5

  16. Ultralytics: YOLOv8. (2023). https://gitcode.com/mirrors/ultralytics/ultralytics

  17. KaiMing, H., XiangYu, Z., ShaoQing, R., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

  18. Shu, L., Qi, L., HaiFang, Q., ::Path Aggregation Network for Instance Segmentation. Conference on Computer Vision and, Recognition, P., et al.: (CVPR). 8759–8768 (2018)

  19. XinYu, L., HouWen, P., NingXin, Z.,EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention. Conference on Computer Vision and, Recognition, P., et al.: (CVPR). 14420–14430 (2023)

  20. Marcelo, G., Roger, F., Victor, A.P.: DSConv: Efficient Convolution Operator. Conference on Computer Vision and Pattern Recognition (CVPR). 5148–5156 (2019)

  21. Han, K., Wang, Y.H., Tian, Q., et al.: GhostNet: more features from cheap operations. Conference on Computer Vision and Pattern Recognition (CVPR). 1577–1586 (2020)

  22. Woo, S., Park, J.: Lee, J. Y., : Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV). 3–19 (2018)

  23. Zheng, Z., Wang, P., Liu, W., et al.: Distance-IoU loss: Faster and better learning for bounding box regression. Comput. Eng. Appl. Proc. AAAI Conf. Artif. Intell. 34(7), 12993–13000 (2020)

    Google Scholar 

  24. Tong, Z., Chen, Y., Xu, Z., et al.: Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv preprint arXiv:2301.10051 (2023)

  25. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

  26. Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detector. European Conference on Computer Vision (ECCV). 21–37. (2016)

  27. Zhou, X., Wang, D.: KräHENBüHL P. Objects as points. Computer Science. 07850(2019) (1904)

Download references

Funding

The National Natural Science Foundation of China (Project No. 12162031).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xinjiang University, Urumqi, Xinjiang, 830047, China

    Sheng Liu, Gulbahar Tohti, Mamtimin Geni, Hualong He, Zhiqiang Wu & Caiwen Liao

  2. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang, 832003, China

    Mamtimin Geni

Authors
  1. Sheng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Gulbahar Tohti
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mamtimin Geni
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hualong He
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zhiqiang Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Caiwen Liao
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Sheng Liu was responsible for algorithmic innovation and thesis writing; Gulbahar ·Tohti was responsible for graphing and data processing; Mamtimin·Geni was responsible for writing comments and providing guidance for the experiments; Hualong He and Zhiqiang Wu mainly helped with the experiments and manipulated the equipment; and CaiWen Liao was responsible for helping with algorithmic operation and data collection.

Corresponding author

Correspondence to Gulbahar Tohti.

Ethics declarations

Ethical approval

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Tohti, G., Geni, M. et al. Micro vibration detection algorithm for rotating machinery based on visual target detection. SIViP 19, 275 (2025). https://doi.org/10.1007/s11760-025-03863-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-025-03863-9

Keywords