A Fault Diagnosis Model Based on Convolution Neural Network for Wind Turbine Rolling Bearing

Yang, Zhichun; Ma, Xiaoshan; Ma, Yuanchi

doi:10.1088/1755-1315/252/3/032039

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Deep learning methods bypass the manual analysis of signals by traditional signal processing methods and directly take the raw signals as input, establishing an 'end-to-end' sample training mode to achieve intelligent recognition of test data [11]. Yang et al [12] proposed a fault diagnosis model based on a convolutional neural network (CNN) for wind turbine rolling bearing. Xu et al [13] developed a hybrid deep-learning model based on CNN and deep forest to extract intrinsic fault features from the images converted by vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Yao,

Wang,

Tao

et al. 2024

Meas. Sci. Technol.

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In intelligent fault diagnosis for rolling bearings, the variable operating conditions of rotating machinery lead to inconsistent distributions between training and testing data, inevitably decreasing fault diagnosis accuracy. To overcome this issue, we propose a bearing fault diagnosis method based on transfer learning with a dual-flow manifold residual network and improved capsule network (DMRCN). Firstly, we introduce a dual-flow manifold residual network (DMResNet) structure consisting of continuous wide-kernel convolution, continuous narrow-kernel convolution, and manifold learning spatial information description blocks. This architecture is utilized to extract low-level features from the data. Then, we present a novel architecture of the capsule network with self-attention output (SAO-CN). We incorporate a self-attention mechanism into the dynamic routing algorithm (SA-DR) to transfer richer high-level features from the low-level capsule layer to the higher-level capsule layer. During training, we propose a loss function known as global-local distribution discrepancy loss (G-L-DDL) to enhance the model's generalization ability and accelerate model convergence. Finally, we validate the performance of DMRCN in cross-condition transfer fault diagnosis on both the CWRU public dataset and a laboratory-built bearing experimental rig dataset. Experimental results demonstrate that DMRCN outperforms other commonly used deep transfer learning methods regarding fault diagnosis across different operating conditions.

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Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Yao,

Wang,

Tao

et al. 2024

Meas. Sci. Technol.

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Robust knowledge transfer for bearing diagnosis in neural network models using multilayer maximum mean discrepancy loss function

Thuan

2024

Meas. Sci. Technol.

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This paper introduces a robust transfer learning method to enhance bearing diagnosis, particularly in cross-machine scenarios. The method trains a shallow neural network using labeled data from a different machine and unlabeled data from the target monitoring machine. To facilitate effective knowledge transfer, a multilayer maximum mean discrepancy loss function is employed, enabling the model to adapt learned features from the source machine to the target machine's unlabeled data. This approach addresses the challenges of low accuracy and robustness often seen in transfer learning, especially when dealing with different machines. Experiments conducted on the Hanoi University of Science and Technology bearing dataset validate the effectiveness of the proposed method. The results show significant improvements in prediction accuracy and robustness, making this method superior to existing transfer learning models for cross-machine bearing diagnosis.

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Imbalance Detection in Low Power Turbine Through Vibration Signals and Convolutional Neural Networks

Rangel-Rodriguez

Estrada-Salazar

Amezquita-Sanchez

et al. 2021

2021 XVII International Engineering Congress (CONIIN)

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A Fault Diagnosis Model Based on Convolution Neural Network for Wind Turbine Rolling Bearing

Cited by 4 publications

References 8 publications

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Robust knowledge transfer for bearing diagnosis in neural network models using multilayer maximum mean discrepancy loss function

Imbalance Detection in Low Power Turbine Through Vibration Signals and Convolutional Neural Networks

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