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. 2025 Aug 24;15(1):31121.
doi: 10.1038/s41598-025-17177-w.

Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM

Lihai Chen  1   2 , Xiaolong Bai  3 , Yonghui He  3 , Dong Jia  3 , Yican Li  3 , Zhenshui Li  3
Affiliations

Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM

Lihai Chen et al. Sci Rep. .

Abstract

The development trend of high precision of mechanical equipment, the reliability of bearings work increasingly demanded. Therefore, it is crucial for the reliable operation of mechanical equipment to evaluate the health status of bearings. It combines IDBO (Improved Dung beetle optimizer) optimised VMD (Variational mode decomposition) and CNN-BiLSTM (convolutional neural network-Bi-directional Long Short-Term Memory) to achieve rolling bearing conformity fault diagnosis and damage assessment. Chaotic mapping, Golden sine algorithm and cosine iteration strategy are introduced to improve the performance of DBO, and the hyperparameters of VMD are optimised using IDBO to improve the signal pre-processing. Feature extraction and fault classification of signals using CNN-BiLSTM is used to compensate for the poor diagnosis of CNN timing signals by BiLSTM instead of Softmax classifier. The HUST dataset is used to discuss the application of signal processing methods and neural network models in bearing composite fault diagnosis. The advantages of the proposed scheme in bearing composite fault and damage assessment are verified, effectively solving the challenge of rolling bearing composite fault diagnosis.

Keywords: CNN-BiLSTM; Damage assessment; Improve dung beetle optimizer (IDBO); Multi-Fault diagnosis; Rolling bearing.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Complex fault diagnosis flow chart.
Fig. 2
Fig. 2
Test function iteration graph.
Fig. 3
Fig. 3
LSTM architecture diagram.
Fig. 4
Fig. 4
BiLSTM architecture diagram.
Fig. 5
Fig. 5
Test rig of HUSTbearing dataset.
Fig. 6
Fig. 6
Photographs of the test bearings.
Fig. 7
Fig. 7
Compound fault signal analysis.
Fig. 8
Fig. 8
The confusion matrix for composite fault diagnosis.
See this image and copyright information in PMC

References

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