A study on fault diagnosis for marine machinery bearings based on multimodal fusion and two-stage discrimination
1
College of Engineering, Shanghai Ocean University, Shanghai 201306, China
Submission date: 2025-11-11
Final revision date: 2026-04-10
Acceptance date: 2026-04-16
Online publication date: 2026-04-16
Publication date: 2026-04-16
Corresponding author
KEYWORDS
TOPICS
ABSTRACT
The marine machinery system operation is in the marine environment, all this bears greatly with this degradation in equipment performance, safety matters too, but in the marine environment which is rather complex, there are noises that make diagnosing faults in the bearing hard for these noises tend to mask or lessen fault features. Then, the conventional signal processing methods are quite sensitive to the selection of parameters for acquiring feature and it is impossible to extract fault feature due to the noisy environment which is unstable. To deal with the problem, this paper offers up a fault diagnosis configuration for bearings that employs multimode union and a twostage distinction technique. There are two stages in the framework: Stage 1 is to deal with the background noise and this not stationary vibration signal about to go through the feature boost process to separate, filter and increase the helpful signal. The second is the fault discriminating module where, using two channels from a deep network with an attention module, channels: chann1: the time domain vector of the sample and the frequency domain vector of the sample chann2: The TF images that the signal have made, meaning adding those images as channels.
ACKNOWLEDGEMENTS
The original vibration signals come from the Case Western University Bearing Data Center website.
FUNDING
This research received no external funding.
REFERENCES (37)
1.
Lv Z, Liu Y, Han X, Liu M. Study on rolling element bearing fault diagnosis methods based on ensemble empirical mode decomposition. Applied Mechanics and Materials. 2014;457:602-607. https://doi.org/10.4028/www.sc....
2.
Liu G, Zhao J, Zhang X. Bearing degradation trend prediction under different operational conditions based on CNN-LSTM. In: IOP Conference Series: Materials Science and Engineering. 2019;612(3):032042. https://doi.org/10.1088/1757-8....
3.
Jia L, Chow T, Yuan Y. GTFE-Net: A gramian time frequency enhancement CNN for bearing fault diagnosis. Engineering Applications of Artificial Intelligence. 2023;119:105794. https://doi.org/10.1016/j.enga....
4.
Ma J, Li H, Chen Y, Wang J, Zou Z. Application of VMD and dynamic wavelet noise reduction techniques in rolling bearing fault diagnosis. In: Journal of Physics: Conference Series. 2023;2528(1):012048. https://doi.org/10.1088/1742-6....
5.
Bayram S, Kaplan K, Kuncan M, Ertunç HM. The effect of bearings faults to coefficients obtaned by using wavelet transform. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU). 2014:991-994. https://doi.org/10.1109/siu.20....
6.
Akcan E, Kuncan M, Kaplan K, Kaya Y. Diagnosing bearing fault location, size, and rotational speed with entropy variables using extreme learning machine. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2024;46(1):4. https://doi.org/10.1007/s40430....
7.
Gao Z, Wei Z, Chen Y, Ying T, Gao H. Bearing fault diagnosis using one-dimensional convolutional neural network. In: 2022 22nd International Conference on Control, Automation and Systems (ICCAS). 2022:158-162. https://doi.org/10.23919/iccas....
8.
Yang J, Han H, Dong X, Wang G, Zhang S. Bearing fault diagnosis grounded in the multi-modal fusion and attention mechanism. Applied Sciences. 2025; 15(3):1531. https://doi.org/10.3390/app150....
9.
Kaya Y, Kuncan F, Ertunç HM. A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences. 2022;30(5):1851-1867. https://doi.org/10.55730/1300-....
10.
You K, Wang P, Huang P, Gu Y. A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis. Reliability Engineering & System Safety. 2025;253:110556. https://doi.org/10.1016/j.ress....
11.
Chen Z, He P. Rolling bearing fault diagnosis considering long-term dependence and time-frequency feature fusion. Int J Automot Manuf Mater. 2025. https://doi.org/10.53941/ijamm....
12.
Li G, Deng C, Wu J, Chen Z, Xu X. Rolling bearing fault diagnosis based on wavelet packet transform and convolutional neural network. Applied Sciences. 2020;10(3):770. https://doi.org/10.3390/app100....
13.
Kuncan M. An intelligent approach for bearing fault diagnosis: combination of 1D-LBP and GRA. IEEE Access. 2020;8:137517-137529. https://doi.org/10.1109/access....
14.
Kaya Y, Kuncan M, Kaplan K, Minaz MR, Ertunç HM. A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental & Theoretical Artificial Intelligence. 2021;33(1):161-178. https://doi.org/10.1080/095281....
15.
Kaplan K, Bayram S, Kuncan M, Ertunç HM. Feature extraction of ball bearings in time-space and estimation of fault size with method of ANN. In: Proceedings of the 16th Mechatronika 2014. 2014 Dec 3-5; Brno, Czech Republic.
16.
Huang F, Zhang K, Li Z, Zheng Q, Ding G, Zhao M, Zhang Y. A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data. Structural Health Monitoring. 2025;24(2):979-997. https://doi.org/10.1177/147592....
17.
Guo Z, Yang M, Huang X. Bearing fault diagnosis based on speed signal and CNN model. Energy Reports. 2022;8:904-913. https://doi.org/10.1016/j.egyr....
18.
Iqbal M, Madan AK, Ahmad N. Vibration and acoustic signal-based bearing fault diagnosis in CNC machine using an improved deep learning. Iran Journal of Computer Science. 2024;7(4):723-733. https://doi.org/10.1007/s42044....
19.
Wang F, Liu X, Deng G, Yu X, Li H, Han Q. Remaining life prediction method for rolling bearing based on the long short-term memory network. Neural Processing Letters. 2019;50(3):2437-2454. https://doi.org/10.1007/s11063....
20.
Qin Y, Shi X. Fault diagnosis method for rolling bearings based on two-channel CNN under unbalanced datasets. Applied Sciences. 2022; 12(17):8474. https://doi.org/10.3390/app121....
21.
Saghi T, Bustan D, Aphale SS. Bearing fault diagnosis based on multi-scale CNN and bidirectional GRU. Vibration. 2022;6(1):11-28. https://doi.org/10.3390/vibrat....
22.
Zhao D, Tian C, Fu Z, Zhong Y, Hou J, He W. Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions. Scientific Reports. 2025;15(1):13035. https://doi.org/10.1038/s41598....
23.
Zarour D, Meziani S, Kedadouche M, Thomas M. Faulty bearing features by variational mode decomposition. Vibroengineering Procedia. 2017;16:29-34. https://doi.org/10.21595/vp.20....
24.
Elouaham S, Dliou A, Latif R, Laaboubi M, Zougagh H, Elkhadiri K. Analysis electroencephalogram signals using denoising and time-frequency techniques. International Journal of Advanced Trends in Computer Science and Engineering. 2021; 10(1). https://doi.org/10.30534/ijatc....
25.
Xiao M, Zhang C, Wen K, Xiong L, Geng G, Wu D. Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise. Journal of Vibroengineering. 2018;20(7):2622-2631. https://doi.org/10.21595/jve.2....
26.
Iqbal M, Madan AK. CNC machine-bearing fault detection based on convolutional neural network using vibration and acoustic signal. Journal of Vibration Engineering & Technologies. 2022;10(5):1613-1621. https://doi.org/10.1007/s42417....
27.
Jiang H, Wang F, Shao H, Zhang H. Rolling bearing fault identification using multilayer deep learning convolutional neural network. Journal of Vibroengineering. 2017;19(1):138-149. https://doi.org/10.21595/jve.2....
28.
Sun B, Hu W, Wang H, Wang L, Deng C. Remaining useful life prediction of rolling bearings based on CBAM-CNN-LSTM. Sensors. 2025;25(2):554. https://doi.org/10.3390/s25020....
29.
Leite VCMN, Borges da Silva JG, Borges da Silva LE, Veloso GFC, Lambert-Torres G, Bonaldi EL, de Oliveira LEL. Experimental bearing fault detection, identification, and prognosis through spectral kurtosis and envelope spectral analysis. Electric Power Components and Systems. 2016;44(18):2121-2132. https://doi.org/10.1080/153250....
30.
Jayasurya S, Geetha S, Abdullah AS, Mishra U. UWE-Net: A deep learning framework for underwater image enhancement integrating CBAM and Charbonnier loss. Procedia Computer Science. 2025;258:689-698. https://doi.org/10.1016/j.proc....
31.
Sarno Filho EP, Santos AD, Sinezio HM, Simas Filho EF, Fernandes Jr ACL, de Seixas JM. Empirical mode decomposition: Theory and applications in underwater acoustics. Journal of Communication and Information Systems. 2022;37(1):145-167. https://doi.org/10.14209/jcis.....
32.
Fu L, Ma Z, Zhang Y, Wang S, Zhang L. An improved bearing fault diagnosis method based on variational mode decomposition and adaptive iterative filtering (VMD-AIF). Journal of Mechanical Science and Technology. 2023;37(4):1601-1612. https://doi.org/10.1007/s12206....
33.
Smith WA, Randall RB. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing. 2015;64:100-131. https://doi.org/10.1016/j.ymss....
34.
Kaplan K, Kaya Y, Kuncan M, Minaz MR, Ertunç HM. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Applied Soft Computing. 2020;87:106019. https://doi.org/10.1016/j.asoc....
35.
Yoo Y, Jo H, Ban SW. Lite and efficient deep learning model for bearing fault diagnosis using the CWRU dataset. Sensors. 2023;23(6):3157. https://doi.org/10.3390/s23063....
36.
Zhou C, Li Y, Ding Z, Li S. Bearing fault diagnosis based on TimesBlock and multi-scale CNN. In: 2024 36th Chinese Control and Decision Conference (CCDC). 2024:5875-5880. https://doi.org/10.1109/ccdc62....
37.
Hoang DT, Kang HJ. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research. 2019;53:42-50. https://doi.org/10.1016/j.cogs....
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
