LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121338 - 121338

Опубликована: Авг. 26, 2023

Язык: Английский

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CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

Information Fusion, Год журнала: 2023, Номер 95, С. 1 - 16

Опубликована: Фев. 11, 2023

Язык: Английский

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Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data

Journal of Industrial Information Integration, Год журнала: 2023, Номер 33, С. 100469 - 100469

Опубликована: Апрель 27, 2023

Язык: Английский

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Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis

Reliability Engineering & System Safety, Год журнала: 2023, Номер 235, С. 109253 - 109253

Опубликована: Март 20, 2023

Язык: Английский

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Multiple-signal defect identification of hydraulic pump using an adaptive normalized model and S transform

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 124, С. 106548 - 106548

Опубликована: Июнь 15, 2023

Язык: Английский

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Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 134, С. 108678 - 108678

Опубликована: Июнь 3, 2024

Язык: Английский

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A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump

Applied Acoustics, Год журнала: 2024, Номер 217, С. 109807 - 109807

Опубликована: Янв. 4, 2024

Язык: Английский

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A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110208 - 110208

Опубликована: Май 29, 2024

Язык: Английский

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Deep Learning-Based Bearing Fault Diagnosis Using a Trusted Multiscale Quadratic Attention-Embedded Convolutional Neural Network

IEEE Transactions on Instrumentation and Measurement, Год журнала: 2024, Номер 73, С. 1 - 15

Опубликована: Янв. 1, 2024

Bearing fault diagnosis is essential for ensuring the safety and reliability of industrial systems. Recently, deep learning approaches, especially convolutional neural network, have demonstrated exceptional performance in bearing diagnosis. However, limited availability training samples has been a persistent issue, leading to significant reduction diagnostic accuracy. Additionally, noise interference or load variation during operation pose challenges To tackle above issues, this paper explores application quadratic neuron with attention-embedded networks introduces trusted multi-scale strategy that fully considers characteristics vibration signals. Building upon these concepts, network proposed faults Experimental results indicate outperforms six stateof-the-art under superimposed on small samples.

Язык: Английский

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A Wavelet Transform-Based Transfer Learning Approach for Enhanced Shaft Misalignment Diagnosis in Rotating Machinery

Electronics, Год журнала: 2025, Номер 14(2), С. 341 - 341

Опубликована: Янв. 17, 2025

Rotating machines are vital for ensuring reliability, safety, and operational availability across various industrial sectors. Among the faults that can affect these machines, shaft misalignment is particularly critical due to its impact on other components connected shaft, making it a key focus diagnostic systems. Misalignment lead significant energy losses, therefore, early detection crucial. Vibration analysis an effective method identifying at stage, enabling corrective actions before negatively impacts equipment efficiency consumption. To improve monitoring efficiency, essential system not only intelligent but also capable of operating in real-time. This study proposes methodology diagnosing by combining wavelet transform feature extraction transfer learning fault classification. The accuracy proposed soft real-time solution validated through comparison with time-frequency transformation techniques networks. includes experimental procedure simulating using laser measurement tool. Additionally, evaluates thermal vibration signature each type multi-sensor monitoring, highlighting effectiveness robustness approach. First, used obtain good representation signal domain. step allows features from signals. Then, network processes different layers identify their severity. combination provides decision-support tool faults, monitoring. tested two datasets: first public dataset, while second was created laboratory simulate alignment demonstrate effect this defect imaging. evaluation carried out criteria methodology. results highlight potential implementing faults.

Язык: Английский

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