Model-Assisted Multi-source Fusion Hypergraph Convolutional Neural Networks for intelligent few-shot fault diagnosis to Electro-Hydrostatic Actuator

Information Fusion, Год журнала: 2023, Номер 104, С. 102186 - 102186

Опубликована: Дек. 11, 2023

Язык: Английский

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Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings

Reliability Engineering & System Safety, Год журнала: 2024, Номер 247, С. 110143 - 110143

Опубликована: Апрель 21, 2024

Язык: Английский

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Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

Knowledge-Based Systems, Год журнала: 2023, Номер 278, С. 110891 - 110891

Опубликована: Авг. 7, 2023

Язык: Английский

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A hybrid deep learning model towards fault diagnosis of drilling pump

Applied Energy, Год журнала: 2024, Номер 372, С. 123773 - 123773

Опубликована: Июнь 26, 2024

This paper proposes a novel method namely WaveletKernelNet-Convolutional Block Attention Module-BiLSTM for intelligent fault diagnosis of drilling pumps. Initially, the random forest is applied to determine target signals that can reflect characteristics Accordingly, Module Net constructed noise reduction and feature extraction based on signals. The Convolutional embedded in WaveletKernelNet-CBAM adjusts weight enhances representation channel spatial dimension. Finally, Bidirectional Long-Short Term Memory concept introduced enhance ability model process time series data. Upon constructing network, Bayesian optimization algorithm utilized ascertain fine-tune ideal hyperparameters, thereby ensuring network reaches its optimal performance level. With hybrid deep learning presented, an accurate real five-cylinder pump carried out results confirmed applicability reliability. Two sets comparative experiments validated superiority proposed method. Additionally, generalizability verified through domain adaptation experiments. contributes safe production oil gas sector by providing robust industrial equipment.

Язык: Английский

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Systematic Review on Fault Diagnosis on Rolling-Element Bearing

Journal of Vibration Engineering & Technologies, Год журнала: 2024, Номер unknown

Опубликована: Апрель 10, 2024

Язык: Английский

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Early bearing fault diagnosis for imbalanced data in offshore wind turbine using improved deep learning based on scaled minimum unscented kalman filter

Ocean Engineering, Год журнала: 2024, Номер 300, С. 117392 - 117392

Опубликована: Март 12, 2024

Язык: Английский

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Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 7, 2025

Industry 4.0 represents the fourth industrial revolution, which is characterized by incorporation of digital technologies, Internet Things (IoT), artificial intelligence, big data, and other advanced technologies into processes. Industrial Machinery Health Management (IMHM) a crucial element, based on (IIoT), focuses monitoring health condition machinery. The academic community has focused various aspects IMHM, such as prognostic maintenance, monitoring, estimation remaining useful life (RUL), intelligent fault diagnosis (IFD), architectures edge computing. Each these categories holds its own significance in context In this survey, we specifically examine research RUL prediction, edge-based architectures, diagnosis, with primary focus domain diagnosis. importance IFD methods ensuring smooth execution processes become increasingly evident. However, most are formulated under assumption complete, balanced, abundant often does not align real-world engineering scenarios. difficulties linked to classifications IMHM have received noteworthy attention from community, leading substantial number published papers topic. While there existing comprehensive reviews that address major challenges limitations field, still gap thoroughly investigating perspectives across complete To fill gap, undertake survey discusses achievements domain, focusing IFD. Initially, classify three distinct perspectives: method processing aims optimize inputs for model mitigate training sample set; constructing model, involves designing structure features enhance resilience challenges; optimizing training, refining process models emphasizes ideal data process. Subsequently, covers techniques related prediction edge-cloud resource-constrained environments. Finally, consolidates outlook relevant issues explores potential solutions, offers practical recommendations further consideration.

Язык: Английский

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Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 122, С. 106138 - 106138

Опубликована: Март 20, 2023

Язык: Английский

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Semi-supervised class incremental broad network for continuous diagnosis of rotating machinery faults with limited labeled samples

Knowledge-Based Systems, Год журнала: 2024, Номер 286, С. 111397 - 111397

Опубликована: Янв. 10, 2024

Язык: Английский

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Multi-modal trajectory forecasting with Multi-scale Interactions and Multi-pseudo-target Supervision

Knowledge-Based Systems, Год журнала: 2024, Номер 296, С. 111903 - 111903

Опубликована: Май 14, 2024

Язык: Английский

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