An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis DOI Creative Commons
Hao Yan, Liangliang Shang,

Wan Chen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 8, 2025

As a critical component of rotating machinery, the operating status rolling bearings is not only related to significant economic interests but also has far-reaching impact on social security. Hence, ensuring an effective diagnosis faults in paramount maintaining operational integrity. This paper proposes intelligent bearing fault method that improves classification accuracy using stacked denoising autoencoder (SDAE) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, (HKELM) initially constructed, leveraging SDAE's deep network architecture for automatic feature extraction. The functions address limitations single by effectively capturing both linear nonlinear patterns data. Subsequently, (HHKELM) refined through enhanced Aquila Optimizer (AO) algorithm, which iteratively optimizes hyperparameter combination. AO algorithm further incorporating chaos mapping, implementing balanced search strategy, fine-tuning parameter [Formula: see text], collectively improve its ability escape local optima conduct global searches, thus strengthening robustness model during optimization. Experimental results CWRU , MFPT JNU datasets demonstrate autoencoder-adaptive (SDAE-AHHKELM) better accuracy, robustness, generalization than KELM other methods.

Language: Английский

On the measurement of resonance frequency of nanoclay-reinforced concrete shell structures validated by experimental datasets via artificial intelligence technique and mathematical modeling DOI

Zhonghong Li,

Yang Bing, Suming Chen

et al.

Measurement, Journal Year: 2025, Volume and Issue: 248, P. 116961 - 116961

Published: Feb. 7, 2025

Language: Английский

Citations

0
The Segmentation of Tunnel Faces in Underground Mines Based on the Optimized YOLOv5 DOI Open Access

Chundi Ma,

Kechao Li,

Jilong Pan

et al.

Minerals, Journal Year: 2025, Volume and Issue: 15(3), P. 255 - 255

Published: Feb. 28, 2025

Tunnel faces in underground mines, as the front line of mining, play an important role both mine safety and mining intelligence. However, engineering quality tunnel is still evaluated based on visual observations by technicians, which cannot guarantee real-time performance. Therefore, there urgent need for a more effective method to detect face engineering. In this study, high-performance accurate segmentation model was developed applying YOLOv5-seg computer vision mine. By optimizing classic Chinese image dataset through Sobel preprocessing improving network structure using SimAM module, good predictive performance achieved segmentation, with values 0.97, 0.89, 0.80, 0.78, respectively, pixel accuracy, Dice coefficient, mask intersection over union (IOU), box IOU test set. And outperforms all YOLOv5 models U-net same task segmentation. Model interpretation visualization further demonstrated positive effect module model, and, finally, results were used evaluate Overall, study’s provide feasible, safe, accurately segmenting mines reliable approach data-driven applications intelligent technology future.

Language: Английский

Citations

0
A computer vision-based real-time monitoring method for swivel bridges spatial rotation DOI
Bei Liu, Ningbo Wang, Can Wang

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117236 - 117236

Published: March 1, 2025

Language: Английский

Citations

0
An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis DOI Creative Commons
Hao Yan, Liangliang Shang,

Wan Chen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 8, 2025

As a critical component of rotating machinery, the operating status rolling bearings is not only related to significant economic interests but also has far-reaching impact on social security. Hence, ensuring an effective diagnosis faults in paramount maintaining operational integrity. This paper proposes intelligent bearing fault method that improves classification accuracy using stacked denoising autoencoder (SDAE) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, (HKELM) initially constructed, leveraging SDAE's deep network architecture for automatic feature extraction. The functions address limitations single by effectively capturing both linear nonlinear patterns data. Subsequently, (HHKELM) refined through enhanced Aquila Optimizer (AO) algorithm, which iteratively optimizes hyperparameter combination. AO algorithm further incorporating chaos mapping, implementing balanced search strategy, fine-tuning parameter [Formula: see text], collectively improve its ability escape local optima conduct global searches, thus strengthening robustness model during optimization. Experimental results CWRU , MFPT JNU datasets demonstrate autoencoder-adaptive (SDAE-AHHKELM) better accuracy, robustness, generalization than KELM other methods.

Language: Английский

Citations

0