An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis

As a critical component of rotating machinery, the operating status of rolling bearings is not only related to significant economic interests but also has a far-reaching impact on social security. Hence, ensuring an effective diagnosis of faults in rolling bearings is paramount in maintaining operat...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 11990 - 26
Hlavní autoři: Yan, Hao, Shang, Liangliang, Chen, Wan, Jiang, Mengyao, lu, Tianqi, Li, Fei
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 08.04.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166/987
639/166/988
Algorithms
Aquila optimizer algorithm
Classification
Extreme learning machine
Fault diagnosis
Humanities and Social Sciences
Learning algorithms
multidisciplinary
Rolling bearings
Science
Science (multidisciplinary)
Stacked denoising autoencoders
Fault diagnosis
Extreme learning machine
Stacked denoising autoencoders
Rolling bearings
Aquila optimizer algorithm
ISSN:2045-2322, 2045-2322
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Shrnutí:As a critical component of rotating machinery, the operating status of rolling bearings is not only related to significant economic interests but also has a far-reaching impact on social security. Hence, ensuring an effective diagnosis of faults in rolling bearings is paramount in maintaining operational integrity. This paper proposes an intelligent bearing fault diagnosis method that improves classification accuracy using a stacked denoising autoencoder (SDAE) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, a hybrid kernel extreme learning machine (HKELM) is initially constructed, leveraging SDAE’s deep network architecture for automatic feature extraction. The hybrid kernel functions address the limitations of single kernel functions by effectively capturing both linear and nonlinear patterns in the data. Subsequently, the hierarchical hybrid kernel extreme learning machine (HHKELM) is refined through an enhanced Aquila Optimizer (AO) algorithm, which iteratively optimizes the kernel hyperparameter combination. The AO algorithm is further enhanced by incorporating chaos mapping, implementing a refined balanced search strategy, and fine-tuning parameter , which collectively improve its ability to escape local optima and conduct global searches, thus strengthening the robustness of the model during parameter optimization. Experimental results on the CWRU , MFPT and JNU datasets demonstrate that stacked denoising autoencoder-adaptive hierarchical hybrid kernel extreme learning machine (SDAE-AHHKELM) has better fault classification accuracy, robustness, and generalization than KELM and other methods.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-94703-w