AGFCN:A bearing fault diagnosis method for high-speed train bogie under complex working conditions

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 110907 - 110907

Опубликована: Фев. 1, 2025

Язык: Английский

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A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis

Reliability Engineering & System Safety, Год журнала: 2024, Номер unknown, С. 110556 - 110556

Опубликована: Окт. 1, 2024

Язык: Английский

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A weighted time embedding transformer network for remaining useful life prediction of rolling bearing

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110399 - 110399

Опубликована: Июль 29, 2024

Язык: Английский

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A roadmap to fault diagnosis of industrial machines via machine learning: A brief review

Measurement, Год журнала: 2024, Номер 242, С. 116216 - 116216

Опубликована: Ноя. 15, 2024

Язык: Английский

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Intelligent fault diagnosis method for rotating machinery based on sample multirepresentation information fusion under limited labeled samples conditions

Measurement, Год журнала: 2025, Номер unknown, С. 117164 - 117164

Опубликована: Фев. 1, 2025

Язык: Английский

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A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder

Structural Health Monitoring, Год журнала: 2024, Номер unknown

Опубликована: Июль 25, 2024

The data measured by the servo motor-bearing system under complex working conditions will present problems such as amplitude fluctuations, unequal impact intervals, and significant differences in distribution, so forth. However, most intelligent fault diagnosis focus on deep learning or transfer learning, which cannot complement knowledge generalized with structural neighbor relationship unknown cross-machine samples. By utilizing Domain Generalized Graph Convolution Autoencoder (DGGCAE), a novel multicross domain method for servo-motor bearing systems can be developed. Specifically, Dirichlet Mixup Distilled augmentations are first employed to augment of feature label layer model training. Accordingly, graph representation multisource is mainly performed developed algorithm. Afterward, convolutional autoencoder extract enough high-dimensional features. Furthermore, DGGCAE’s classification loss discrimination calculated narrow distribution gap among domains. Finally, simulation test bench (called motor-Cylindrical roller from Nanjing University Science Technology) has validated development diagnostic method.

Язык: Английский

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Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings

Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 220, С. 111679 - 111679

Опубликована: Июль 2, 2024

Язык: Английский

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A health assessment method with attribute importance modeling for complex systems using belief rule base

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110387 - 110387

Опубликована: Июль 29, 2024

Язык: Английский

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MPNet: A lightweight fault diagnosis network for rotating machinery

Measurement, Год журнала: 2024, Номер 239, С. 115498 - 115498

Опубликована: Авг. 11, 2024

Язык: Английский

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A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples

Reliability Engineering & System Safety, Год журнала: 2024, Номер 252, С. 110429 - 110429

Опубликована: Авг. 12, 2024

Язык: Английский

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