RHCBAM-based stacked denoising autoencoder for wind turbine misalignment fault detection
Saved in:
| Title: | RHCBAM-based stacked denoising autoencoder for wind turbine misalignment fault detection |
|---|---|
| Authors: | Dian Long, Yancai Xiao, Aixin Dai, Teng Fan |
| Source: | Measurement Science and Technology. 36:086143 |
| Publisher Information: | IOP Publishing, 2025. |
| Publication Year: | 2025 |
| Description: | As wind power generation technology continues to advance, issues related to misalignment faults in wind turbine drive systems have garnered increasing attention. Under actual operating conditions, vibration signals obtained from wind turbine often exhibit low-frequency, nonlinear, and non-steady-state characteristics, and are easily influenced by factors such as fluctuations in wind speed, mechanical friction, and changes in environmental temperature. This complex background significantly reduces the stability and accuracy of traditional fault diagnosis methods. To address these challenges, this paper proposes a novel fault diagnosis model that combines the residual hybrid convolutional attention module with the stacked denoising autoencoder (SDAE), and designs a multi-stage denoising training strategy (MSD). The RHCBAM integrates multi-scale convolutional structures, multi-channel attention mechanisms, and residual connection mechanisms, significantly enhancing the ability to capture weak and complex fault features and improving training stability. By enhancing feature extraction depth, it addresses issues such as gradient vanishing and insufficient fusion capabilities that traditional DAE models encounter when processing complex non-stationary signals. The proposed MSD achieves robustness enhancement during the unsupervised training phase by constructing typical noise scenarios such as Gaussian white noise, periodic interference, and pulse noise, combined with noise samples of different intensity levels, significantly improving its generalization ability in real complex environments and effectively addressing multi-source, multi-level noise pollution issues in wind turbine operating environments. Extensive experiments on wind turbine drive system fault test benches and the CWRU bearing fault dataset demonstrate that the proposed method achieves fault identification accuracy rates of 99.64% and 99.70%, respectively, significantly outperforming existing mainstream deep learning models, thereby validating its diagnostic performance and key feature extraction capabilities in complex noise environments. |
| Document Type: | Article |
| ISSN: | 1361-6501 0957-0233 |
| DOI: | 10.1088/1361-6501/adfcfd |
| Rights: | IOP Copyright Policies |
| Accession Number: | edsair.doi...........ffdaa16db951e2c67a2286a1e15fc77b |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | As wind power generation technology continues to advance, issues related to misalignment faults in wind turbine drive systems have garnered increasing attention. Under actual operating conditions, vibration signals obtained from wind turbine often exhibit low-frequency, nonlinear, and non-steady-state characteristics, and are easily influenced by factors such as fluctuations in wind speed, mechanical friction, and changes in environmental temperature. This complex background significantly reduces the stability and accuracy of traditional fault diagnosis methods. To address these challenges, this paper proposes a novel fault diagnosis model that combines the residual hybrid convolutional attention module with the stacked denoising autoencoder (SDAE), and designs a multi-stage denoising training strategy (MSD). The RHCBAM integrates multi-scale convolutional structures, multi-channel attention mechanisms, and residual connection mechanisms, significantly enhancing the ability to capture weak and complex fault features and improving training stability. By enhancing feature extraction depth, it addresses issues such as gradient vanishing and insufficient fusion capabilities that traditional DAE models encounter when processing complex non-stationary signals. The proposed MSD achieves robustness enhancement during the unsupervised training phase by constructing typical noise scenarios such as Gaussian white noise, periodic interference, and pulse noise, combined with noise samples of different intensity levels, significantly improving its generalization ability in real complex environments and effectively addressing multi-source, multi-level noise pollution issues in wind turbine operating environments. Extensive experiments on wind turbine drive system fault test benches and the CWRU bearing fault dataset demonstrate that the proposed method achieves fault identification accuracy rates of 99.64% and 99.70%, respectively, significantly outperforming existing mainstream deep learning models, thereby validating its diagnostic performance and key feature extraction capabilities in complex noise environments. |
|---|---|
| ISSN: | 13616501 09570233 |
| DOI: | 10.1088/1361-6501/adfcfd |