An Adaptive Activation Transfer Learning Approach for Fault Diagnosis

Chen, Yongyi; Zhang, Dan; Zhu, Kunpeng; Yan, Ruqiang

doi:10.1109/tmech.2023.3243533

Cited by 28 publications

(4 citation statements)

References 27 publications

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“…According to the weighting principle and training objectives of the adaptive weighting matrix W AWM , the objective function of the adaptive weighting module is constructed, and the align is shown in align (17). The first item in the align is the reconstruction error between the input signal and the reconstructed signal, which meets the training objective 1.…”

Section: Adaptive Weighting Model Of Signal-noise Feature Changementioning

confidence: 99%

“…If the noise reduction method for strong noise is used to process the weak noise signal, the accuracy of the reconstructed signal will be reduced, which is not conducive to accurately establishing the fault degradation characteristics. To solve the problem that the performance of noise reduction methods decreases when the application scenarios change, researchers generally adopt adaptive noise reduction [15], convolutional neural networks [16] and transfer learning [17]. This paper is inspired by the above references.…”

Section: Introductionmentioning

confidence: 99%

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Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

Yu,

Li,

2024

Meas. Sci. Technol.

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In the prediction of bearing fault remaining useful life (RUL), the identification and feature extraction of early bearing faults are very important. In order to improve the accuracy of early fault RUL prediction, a bearing fault RUL prediction model based on weighted variable loss degradation characteristics is proposed. The model is composed of a stack denoising autoencoder (SDAE) module guided by variable loss, a signal-to-noise feature adaptive weighting module and a Long-short Term Memory (LSTM) degradation characteristics extraction and regression output module. Firstly, this model improves the ability of SDAE model to extract weak fault features by ascending dimension learning and variable loss function. Then, an adaptive weighting matrix is generated according to the test signal to modulate the weight vector of SDAE. Finally, the hidden layer features of SDAE were input into LSTM model to extract the bearing state degradation features and realize the RUL prediction of bearing faults. The experimental results show that the proposed model can accurately predict the RUL of the test data in the early fault stage and the fault development stage. The proposed model can give early fault warning to the bearing state.

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Section: Adaptive Weighting Model Of Signal-noise Feature Changementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

Yu,

Li,

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Building models that are capable of data analysis, optimization [22,23], spatial encoding [24], spatial ability [25][26][27], learning content management systems [28,29], prediction, and other tasks is the aim of machine learning [30,31] and its subsets, including federated learning [32][33][34], recurrent neural networks [35], deep learning networks, etc. In this regard, Michael Deferard and his colleagues first introduced the fundamental notion of the graph convolutional network.…”

Section: General Model Of Graph Convolutional Networkmentioning

confidence: 99%

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Zare,

Rahmani,

Khaleghi

et al. 2024

Bioengineering

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Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.

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“…In transfer learning, although the source task and the target task are different, knowledge related to the target task can be mined from different data of different source tasks and help the learning of the target task [26]. The weights trained in the laboratory simulation pipeline leakage database can be retained through model migration, and then the final model can be obtained by fine-tuning the model using the target domain leakage fault data.…”

Section: Introductionmentioning

confidence: 99%

Pipeline leakage aperture identification method based on pseudolabel learning

Yuan¹,

Lang

Zhang

et al. 2023

Meas. Sci. Technol.

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Aiming at the problem of insufficient label data in the pipeline leak detection field, this paper proposes a pseudolabel adaptive learning method (PLALM) based on multiscale convolutional neural network (MSCNN) with the idea of transfer learning for pipeline leak aperture identification. First, the convolutional and pooling layers for transfer learning feature extraction are improved by using a dual-channel MSCNN. Second, the KL divergence function after dimensionality reduction is used to calculate the distribution distance between the source domain and the target domain to improve the robustness of distribution alignment in high-noise environments. In addition, considering the interference of pseudo-label noise, this paper develops a pseudolabel(PL) dynamic threshold to achieve the purpose of PL adaptive updating. Compared with the fixed threshold, the improved pseudolabel learning(PLL) can effectively improve the prediction accuracy of the model. The effectiveness of the method proposed in this paper is verified by predicting pipeline leakage conditions at different distances and under different pressures. The comparative analysis results show that the method in this paper is superior to other transfer learning methods in terms of prediction accuracy, stability, and convergence speed.

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An Adaptive Activation Transfer Learning Approach for Fault Diagnosis

Cited by 28 publications

References 27 publications

Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Pipeline leakage aperture identification method based on pseudolabel learning

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