Online Fault Diagnosis of Industrial Robot Using IoRT and Hybrid Deep Learning Techniques: An Experimental Approach

IEEE Internet of Things Journal, Год журнала: 2024, Номер 11(19), С. 31422 - 31437

Опубликована: Июнь 24, 2024

Язык: Английский

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A review: the application of generative adversarial network for mechanical fault diagnosis

Measurement Science and Technology, Год журнала: 2024, Номер 35(6), С. 062002 - 062002

Опубликована: Март 19, 2024

Abstract Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With rapid development deep learning technology, methods based on big data-driven provide a new perspective machinery. However, equipment operates in condition most time, resulting collected data being imbalanced, which affects performance diagnosis. As approach generating data, generative adversarial network (GAN) can effectively address issues limited and imbalanced practical engineering applications. This paper provides comprehensive review GAN Firstly, GAN-based diagnosis, basic theory various variants (GANs) are briefly introduced. Subsequently, GANs summarized categorized from labels models, corresponding applications outlined. Lastly, limitations current research, future challenges, trends selecting application discussed.

Язык: Английский

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Multi-distribution mixture generative adversarial networks for fitting diverse data sets

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123450 - 123450

Опубликована: Фев. 9, 2024

Язык: Английский

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Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110380 - 110380

Опубликована: Июль 26, 2024

Язык: Английский

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Adaptive Fusion Transfer Learning-based Digital Multitwin-assised Intelligent Fault Diagnosis

Knowledge-Based Systems, Год журнала: 2024, Номер 297, С. 111923 - 111923

Опубликована: Май 21, 2024

Язык: Английский

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Synthetic image generation for effective deep learning model training for ceramic industry applications

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 143, С. 110019 - 110019

Опубликована: Янв. 14, 2025

Язык: Английский

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KDN: A class-added continual learning framework for cross-machine fault diagnosis with limited samples

Mechanical Systems and Signal Processing, Год журнала: 2025, Номер 227, С. 112379 - 112379

Опубликована: Янв. 28, 2025

Язык: Английский

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Fault diagnosis for ion mill etching machine cooling system based on omni-dimensional dynamic convolution and dynamic spatial pyramid pooling

Nondestructive Testing And Evaluation, Год журнала: 2025, Номер unknown, С. 1 - 32

Опубликована: Янв. 29, 2025

Язык: Английский

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Rolling Bearing Dynamics Simulation Information-Assisted Fault Diagnosis with Multi-Adversarial Domain Transfer Learning

Lubricants, Год журнала: 2025, Номер 13(3), С. 116 - 116

Опубликована: Март 7, 2025

To address the issues of negative transfer and reduced stability in learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial algorithm based on dynamics simulation data is proposed. Firstly, constructs both a global domain classifier subdomain classifier. In classifier, simulated vibration signal, which contains rich label information, generated by constructing dynamic equations to replace prediction target data, thereby achieving alignment marginal conditional distributions. Simultaneously, improved loss function with embedded maximum mean discrepancy designed reduce feature distribution gap between source data. Finally, weight allocation mechanism samples developed promote positive suppress transfer. Experiments were conducted using Paderborn University dataset Huazhong Science Technology dataset, accuracy rates 89.457% 96.436%, respectively. The results show that, comparison existing cross-domain methods, proposed method demonstrates significant improvements diagnostic stability, demonstrating its superiority operational conditions.

Язык: Английский

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Dual-stage manifold preserving mixed supervised learning for bogie fault diagnosis under variable conditions

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 149, С. 110512 - 110512

Опубликована: Март 12, 2025

Язык: Английский

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