Generalized reassigning transform: algorithm and applications DOI
Dezun Zhao, Xiaofan Huang, Tianyang Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: unknown, P. 110677 - 110677

Published: Nov. 1, 2024

Language: Английский

CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery DOI
Yadong Xu, Ke Feng, Xiaoan Yan

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 95, P. 1 - 16

Published: Feb. 11, 2023

Language: Английский

Citations

103
LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention DOI
Shen Yan, Haidong Shao, Jie Wang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121338 - 121338

Published: Aug. 26, 2023

Language: Английский

Citations

103
Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data DOI
Chao Yang, Baoping Cai,

Qibing Wu

et al.

Journal of Industrial Information Integration, Journal Year: 2023, Volume and Issue: 33, P. 100469 - 100469

Published: April 27, 2023

Language: Английский

Citations

78
Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis DOI
Yutong Dong, Hongkai Jiang, Zhenghong Wu

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 235, P. 109253 - 109253

Published: March 20, 2023

Language: Английский

Citations

46
Multiple-signal defect identification of hydraulic pump using an adaptive normalized model and S transform DOI
Yong Zhu, Shengnan Tang, Shouqi Yuan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 124, P. 106548 - 106548

Published: June 15, 2023

Language: Английский

Citations

43
Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery DOI
Shengnan Tang, Jingtao Ma,

Zhengqi Yan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108678 - 108678

Published: June 3, 2024

Language: Английский

Citations

32
A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump DOI
Shengnan Tang, Boo Cheong Khoo, Yong Zhu

et al.

Applied Acoustics, Journal Year: 2024, Volume and Issue: 217, P. 109807 - 109807

Published: Jan. 4, 2024

Language: Английский

Citations

28
A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis DOI

Shuaiyu Zhao,

Yiling Duan, Nitin Roy

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 249, P. 110208 - 110208

Published: May 29, 2024

Language: Английский

Citations

24
Deep Learning-Based Bearing Fault Diagnosis Using a Trusted Multiscale Quadratic Attention-Embedded Convolutional Neural Network DOI
Yuheng Tang, Chaoyong Zhang, Jianzhao Wu

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2024, Volume and Issue: 73, P. 1 - 15

Published: Jan. 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.

Language: Английский

Citations

20
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

16