Multi-layer adaptive convolutional neural network unsupervised domain adaptive bearing fault diagnosis method

Cui, Jie; Li, Yanfeng; Zhang, Qianqian; Wang, Zhijian; Du, Wenhua; Wang, Junyuan

doi:10.1088/1361-6501/ac6ab3

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…The following methods replace the objective optimization function: the objective function based on the combination of CE loss and LMMD [22], Focal Loss [38] and LMMD, Gradient Harmonizing Mechanism (GHM) [39] and LMMD. The proposed Lat-k algorithm combined with CORAL [40] and MK-MMD [41], respectively. The objective optimization function of the comparative method (CE + LMMD) is shown in equation ( 13), and the remaining four comparisons are shown individually in equations ( 19)- (22).…”

Section: Introduction Of Datasets and Comparative Methodsmentioning

confidence: 99%

Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings

Huo,

Deng,

Zhang

et al. 2023

Meas. Sci. Technol.

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Data on the vibration signals collected from rolling bearings mostly belongs to health conditions, leading to an imbalanced data distribution. In addition, frequent switching of operating conditions results in unlabeled data collected under a specific working condition. To address these challenges, this paper proposes a novel network for cross-domain unsupervised fault diagnosis of rolling bearings considering the imbalanced data. First, a multiscale parallel features extraction (MPFE) is developed, which can fully mine each fault type's inherent invariant domain features with a high value for fault identification from original data. Second, a squeeze-and-excitation attention mechanism (SEAM) is constructed to enhance features conducive to model classification and suppress redundant features. Finally, a new subdomain adaption algorithm is presented to adaptively align the relevant imbalanced subdomain data of two domains. The proposed method was validated on multiple unsupervised cross-domain diagnostic tasks on two bearing datasets. Experimental results manifest that the proposed method has stable generalization performance and excellent robustness.

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Section: Introduction Of Datasets and Comparative Methodsmentioning

confidence: 99%

Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings

Huo,

Deng,

Zhang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Yao et al [8] proposed a stacked inverse residual convolutional neural network intelligent bearing fault diagnosis method, which improved the diagnosis speed of the model and the diagnosis effect in noisy environment. Cui et al [9] presented a multi-layer adaptive convolutional neural network bearing fault method, which enhanced the feature learning ability of the model and achieved high fault diagnosis accuracy under variable working conditions. Li et al [10] put forward an improved convolutional neural network fault diagnosis method, which effectively improved the feature extraction and generalization ability of bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for rolling bearings based on BICNN under complex operating conditions

Duan,

Shi,

Lei

et al. 2024

Preprint

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To address the issues of poor noise resistance and insufficient generalization performance in traditional fault diagnosis methods, an end-to-end rolling bearing fault diagnosis method based on Bidirectional Interactive Convolutional Neural Network (BICNN) is proposed. Firstly, the bearing vibration signal is directly input into the wide convolutional kernel for rapid feature extraction, reducing the interference of high-frequency noise. Secondly, a modified Rectified Linear Unit (M-ReLU) activation function is designed to solve the problem of "neuron death" in the ReLU activation function. Then, a bidirectional interactive feature extraction module is constructed, and the features extracted are input into the bidirectional interactive feature extraction module to capture the channel and spatial feature information simultaneously. Next, the extracted information is imported the presented feature enhancement module to achieve more valuable information transmission and accumulation. Finally, a small convolutional kernel is applied to further extract feature information, and a global average pooling layer is used to replace the fully connected layer, reducing the number of parameters while avoiding the problem of model overfitting. The Softmax is utilized to classify the types of bearing faults. Two different datasets are adopted to validate the fault diagnosis performance of the proposed model under − 4dB signal-to-noise ratio and variable working conditions. Experimental results show that compared with other fault diagnosis methods, the proposed model has higher fault diagnosis accuracy, stronger noise resistance, and generalization ability.

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“…Yang et al [17] designed a multilayer domain adaptive approach to narrow down the distribution differences of different domain features, and further achieved the transfer of laboratory bearing diagnosis models to locomotive bearings. Cui et al [18] used Log-Euclidean distance to improve domain alignment, and realized multiple fault diagnosis of gearbox bearings under variable working conditions. However, domain adaptive transfer learning is mostly applied to fault diagnosis, and rarely used to wind turbine condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

Transfer condition monitoring across wind turbines using feature alignment and parameter fine-tuning

Chen

Qiao

Xie

et al. 2023

Meas. Sci. Technol.

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Transfer condition monitoring across wind turbines still exists two issues. (1) The model of single turbine trained is directly used for other ones in the wind farm to result in low monitoring accuracy because of individual features of each turbine; (2) if the model is trained separately for each turbine, it is not only time-consuming and inefficient, but also is difficult to transfer across turbines. For solving the two issues, a transfer condition monitoring method across wind turbines using feature alignment and parameter fine-tuning is proposed. First, the representative normal turbine in a wind farm is screened by calculating the correlation between the monitoring parameters of wind turbines; Second, the input features are constructed by using the massive normal SCADA data of the representative turbine. The intelligent condition monitoring model of wind turbines is designed by using convolutional neural networks (CNN) for spatial features and Long-term and short-term memory networks (LSTM) for time features; . Finally, feature alignment is performed using the normal data of the representative turbine and a large amount of historical normal data from other turbines to reduce data distribution differences. The aligned data is inputted into pre-training monitoring models for parameter fine-tuning. The personalized features of each turbine are incorporated to achieve high-precision transfer condition monitoring across turbines. The proposed method is verified by the real-world turbine data from a wind farm cooperated with us. The results show that the proposed transfer condition monitoring method using feature alignment and parameter fine-tuning can accurately monitor and identify the health states of wind turbines.

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Multi-layer adaptive convolutional neural network unsupervised domain adaptive bearing fault diagnosis method

Cited by 7 publications

References 32 publications

Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings

Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings

Fault diagnosis method for rolling bearings based on BICNN under complex operating conditions

Transfer condition monitoring across wind turbines using feature alignment and parameter fine-tuning

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