A Novel Cross-Domain Data Augmentation and Bearing Fault Diagnosis Method Based on an Enhanced Generative Model DOI
Shilong Sun, Hao Ding, Haodong Huang

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2024, Volume and Issue: 73, P. 1 - 9

Published: Jan. 1, 2024

In actual industrial production, differences in production conditions lead to variations the collected data distribution. This gives rise a particular problem: while one set of has complete status available, another only possesses from healthy state. Differences result limitations for diagnosing new condition. To address this challenge, method based on envelope order spectra generation is proposed. Initially, and analysis conducted raw vibration align across different domains extract domain-independent signal components—the spectra. Subsequently, an enhanced Variational Autoencoder Generative Adversarial Network (VAEGAN) trained using The model then employed generate synthetic spectra, serving as augmentation working conditions, thereby achieving cross-domain augmentation. Next, augmented used train generic fault classification, enabling diagnosis. Finally, proposed approach validated by testing it with real Experimental results demonstrate that can reliable fake under diverse accomplishing diagnosis preserving privacy.

Language: Английский

Model-Assisted Multi-source Fusion Hypergraph Convolutional Neural Networks for intelligent few-shot fault diagnosis to Electro-Hydrostatic Actuator DOI
Xiaoli Zhao, Xingjun Zhu, Jiahui Liu

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 104, P. 102186 - 102186

Published: Dec. 11, 2023

Language: Английский

Citations

46

Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings DOI
Jinxin Wu, Deqiang He, Jiayi Li

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 247, P. 110143 - 110143

Published: April 21, 2024

Language: Английский

Citations

40

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

Ali Saeed,

Muazzam A. Khan, Usman Akram

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 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.

Language: Английский

Citations

2

Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis DOI
Zhe Wang, Zhiying Wu, Xingqiu Li

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 278, P. 110891 - 110891

Published: Aug. 7, 2023

Language: Английский

Citations

40

A hybrid deep learning model towards fault diagnosis of drilling pump DOI Creative Commons
Junyu Guo, Yulai Yang, He Li

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123773 - 123773

Published: June 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.

Language: Английский

Citations

17

Early bearing fault diagnosis for imbalanced data in offshore wind turbine using improved deep learning based on scaled minimum unscented kalman filter DOI
Haihong Tang, Kun Zhang, Bing Wang

et al.

Ocean Engineering, Journal Year: 2024, Volume and Issue: 300, P. 117392 - 117392

Published: March 12, 2024

Language: Английский

Citations

14

Systematic Review on Fault Diagnosis on Rolling-Element Bearing DOI

M. Pandiyan,

T. Narendiranath Babu

Journal of Vibration Engineering & Technologies, Journal Year: 2024, Volume and Issue: unknown

Published: April 10, 2024

Language: Английский

Citations

14

Mitigating overconfidence in unknown sample predictions: A confidence-enhanced one-versus-all network for open-set transfer fault diagnosis DOI
Yang Liu, Yaowei Shi, Minqiang Deng

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113013 - 113013

Published: Jan. 1, 2025

Language: Английский

Citations

1

Cross-sensor contrastive learning-based pre-training for machinery fault diagnosis under sample-limited conditions DOI
Hao Hu, Yue Ma, Ruoxue Li

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113075 - 113075

Published: Feb. 1, 2025

Language: Английский

Citations

1

Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network DOI
Xiaorong Liu, Jie Wang, Sa Meng

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 122, P. 106138 - 106138

Published: March 20, 2023

Language: Английский

Citations

19