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Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network Cover

Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network

Polish Maritime Research
Volume 31 (2024): Issue 3 (September 2024)
By: Congyue Li and  Dexin Cui  
Open Access
|Aug 2024

References

  1. [1] Shi QG, Hu YH, Yan GH. Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis. Polish Maritime Research, vol. 30, no. 1, pp. 98-111, 2023, https://doi.org/10.2478/pomr-2023-0010.
    Search in Google ScholarBack to article
  2. [2] Gültekin Ö, Çinar E, Özkan K, Yazıcı A. A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images. Neural Computing and Applications, vol. 34, no. 6, pp. 4803-4812, 2022, https://doi.org/10.1007/S00521-021-06668-2.
    Search in Google ScholarBack to article
  3. [3] Anwarsha A, Narendiranath B. A review on the role of tunable Q-Factor wavelet transform in fault diagnosis of rolling element bearings. Journal of Vibration Engineering & Technologies, vol.10, no. 5, pp. 1793-1808, 2022, https://doi.org/10.1007/S42417-022-00484-1.
    Search in Google ScholarBack to article
  4. [4] He L, Liu Q, Jiang Z. Combined underdamped bistatic stochastic resonance for weak signal detection and fault diagnosis under wavelet transform. Fluctuation and Noise Letters, vol. 22, no. 1, pp. 2350007, 2023, https://doi.org/10.1142/S0219477523500074.
    Search in Google ScholarBack to article
  5. [5] Cheng Y, Lin M, Wu J, Zhu H, Shao X. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowledge-Based Systems, vol. 216, pp. 106796, 2021, https://doi.org/10.1016/J.KNOSYS.2021.106796.
    Search in Google ScholarBack to article
  6. [6] Shafiullah M, AlShumayri KA, Alam MS. Machine learning tools for active distribution grid fault diagnosis. Advances in Engineering Software, vol. 173, pp. 103279, 2022, https://doi.org/10.1016/J.ADVENGSOFT.2022.103279.
    Search in Google ScholarBack to article
  7. [7] Zhou X, Wang X, Wang H, Cao L, Xing Z, Yang Z. Rotor fault diagnosis method based on VMD symmetrical polar image and fuzzy neural network. Applied Sciences, vol. 13, no. 2, pp. 1134, 2023, https://doi.org/10.3390/APP13021134.
    Search in Google ScholarBack to article
  8. [8] Mohammed EAK, Ameur FA, Ahmed HB, Noureddine B, Azeddine B. Bearing fault diagnosis of a PWM inverter fed-Induction motor using an improved short time Fourier transform. Journal of Electrical Engineering & Technology, vol. 14, no. 3, pp. 1201-1210, 2019, https://doi.org/10.1007/s42835-019-00096-y
    Search in Google ScholarBack to article
  9. [9] Hou SZ, Guo W, Wang Z, Liu Y. Deep-learning-based fault type identification using modified CEEMDAN and image augmentation in distribution power grid. IEEE Sensors Journal, vol. 22, no. 2, pp. 1583-1596, 2022, https://doi.org/10.1109/JSEN.2021.3133352.
    Search in Google ScholarBack to article
  10. [10] Liu Z, Peng D, Zuo M, Xia J, Qin Y. Improved Hilbert-Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings. ISA transactions, vol. 125, pp. 426-444, 2021, https://doi.org/10.1016/J.ISATRA.2021.07.011.
    Search in Google ScholarBack to article
  11. [11] Ji Y, Wang H. A revised Hilbert-Huang transform and its application to fault diagnosis in a rotor. System Sensors (Basel, Switzerland), vol. 18, no. 12, pp. 4329, 2018, https://doi.org/10.3390/s18124329.
    Search in Google ScholarBack to article
  12. [12] Hou SZ, Guo W. Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network. IET Generation, Transmission & Distribution, vol. 16, no. 4, pp. 737-749, 2021, https://doi.org/10.1049/GTD2.12324.
    Search in Google ScholarBack to article
  13. [13] McFadden PD. A revised model for the extraction of periodic waveforms by time domain averaging. Mechanical Systems and Signal Processing, vol. 1, no. 1, pp. 83-95, 1987, https://doi.org/10.1016/0888-3270(87)90085-9.
    Search in Google ScholarBack to article
  14. [12] Yin N, Meng Z, Guan Y, Fan F. An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement. Measurement Science and Technology, vol. 33, no. 5, pp. 055004, 2022, https://doi.org/10.1088/1361-6501/AC3D08.
    Search in Google ScholarBack to article
  15. [15] Jong MH, Byeng DY, Hyunseok O, Bongtae H, Yoongho J, Jungho P. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mechanical Systems and Signal Processing, vol. 70, pp. 161-175, 2016, https://doi.org/10.1016/j.ymssp.2015.09.040.
    Search in Google ScholarBack to article
  16. [16] Tofighi NS, Alavi H, Ohadi A. Incipient fault detection of helical gearbox based on variational mode decomposition and time synchronous averaging. Structural Health Monitoring, vol. 22, no. 2, pp. 1494-1512, 2023, https://doi.org/10.1177/14759217221108489.
    Search in Google ScholarBack to article
  17. [17] Huang ZF, Sun KC, Wei DH, Mao HL, Li XX, Qian X. Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching. Measurement Science and Technology, vol. 32, no. 10, pp. 105010, 2021, https://doi:10.1088/1361-6501/AC02F6.
    Search in Google ScholarBack to article
  18. [18] Xu ZF, Zhang CC, Liu SH, Zhang W, Zhang YF. Research on fault diagnosis of rolling bearings in roller-to-roller printing units based on Siamese network. Journal of Low Frequency Noise, Vibration and Active Control, vol. 42, no. 1, pp. 403-419, 2023, https://doi.org/10.1177/14613484221119897.
    Search in Google ScholarBack to article
  19. [19] Liu BW, Chai Y, Jiang YT, Wang YM. Industrial fault detection based on discriminant enhanced stacking auto-encoder model. Electronics, vol. 11, no. 23, pp. 3993, 2022, https://doi.org/10.3390/ELECTRONICS11233993.
    Search in Google ScholarBack to article
  20. [20] Tang JH, Wu JM, Hu BB, Liu J. Towards a fault diagnosis method for rolling bearing with Bi-directional deep belief network. Applied Acoustics, vol. 192, 2022, https://doi.org/10.1016/J.APACOUST.2022.108727.
    Search in Google ScholarBack to article
  21. [21] Tang SN, Zhu Y, Yuan SQ. An adaptive deep learning model towards fault diagnosis of hydraulic piston pump using pressure signal. Engineering Failure Analysis, vol. 138, 2022, https://doi.org/10.1016/J.ENGFAILANAL.2022.106300.
    Search in Google ScholarBack to article
  22. [22] Xu ZB, Wang Y, Xiong W, Wang ZG. A novel attentional feature fusion with inception based on capsule network and application to the fault diagnosis of bearing with small data samples. Machines, vol. 10, no. 9, pp. 789, 2022, https://doi.org/10.3390/MACHINES10090789.
    Search in Google ScholarBack to article
  23. [23] Zhang K, Tang BQ, Deng L, Liu XL. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. Measurement, vol. 179, 2021, https://doi.org/10.1016/J.MEASUREMENT.2021.109491.
    Search in Google ScholarBack to article
  24. [24] Wang HQ, Li S, Song LY, Cui LL. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals. Computers in Industry, vol. 105 pp. 182-190, 2019, https://doi.org/10.1016/j.compind.2018.12.013.
    Search in Google ScholarBack to article
  25. [25] G. H. Yan, Y. H. Hu, Q. G. Shi, “A convolutional neural network-based method of inverter fault diagnosis in a ship’s DC electrical system,” Polish Maritime Research, vol. 29, no. 4, pp. 105-114, 2022, https://doi.org/10.2478/pomr-2022-0048.
    Search in Google ScholarBack to article
  26. [26] Li CY, Hu YH, Jiang JW, Yan GH. Deep learning-based fault diagnosis for marine centrifugal fan. Polish Maritime Research, vol. 30, no. 1, pp.112-120, 2022, https://doi.org/10.2478/pomr-2023-0011.
    Search in Google ScholarBack to article
  27. [27] Liu FY, Li WJ, Wu YZ, He YH, Li TY. Fault diagnosis of imbalance and misalignment in rotor-bearing systems using deep learning. Polish Maritime Research, vol. 31, no. 1, pp. 102-113, 2024, https://doi.org/10.2478/pomr-2024-0011.
    Search in Google ScholarBack to article
  28. [28] Wen L, Li XY, Gao L, Zhang YY. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, vol. 65, no.7, pp. 5990-5998, 2018, https://doi.org/10.1109/tie.2017.2774777.
    Search in Google ScholarBack to article
  29. [29] Cui YB, Wang RJ, Si YP, et al. T-type inverter fault diagnosis based on GASF and improved AlexNet. Energy Reports, vol. 9, pp. 2718-2731, 2023, https://doi.org/10.1016/J.EGYR.2023.01.095.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/pomr-2024-0038 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
Journal RSS Feed
Language: English
Page range: 85 - 93
Published on: Aug 21, 2024
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
multi-scale time-domain averaging decomposition,
signal-to-conversion,
CNN,
marine diesel engine,
fault diagnosis
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other,
Geosciences,
Atmospheric science and climatology,
Life sciences,
Life sciences, other

© 2024 Congyue Li, Dexin Cui, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

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