Time- and frequency-domain fusion for source-free adaptation fault diagnosis DOI
Yu Gao,

Z. Zhang,

Bingquan Chen

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102875 - 102875

Published: Jan. 1, 2025

Language: Английский

Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet DOI Creative Commons

Faisal Saleem,

Zahoor Ahmad, Muhammad Siddique

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1112 - 1112

Published: Feb. 12, 2025

Effective leak detection and size identification are essential for maintaining the operational safety, integrity, longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, excessive computational costs, which limits their feasibility real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline approach, integrating Empirical Wavelet Transform (EWT) adaptive frequency decomposition with customized one-dimensional DenseNet architecture achieve precise classification. The methodology begins EWT-based signal segmentation, isolates meaningful bands enhance leak-related feature extraction. To further improve quality, thresholding denoising techniques applied, filtering out low-amplitude while preserving critical diagnostic information. denoised signals processed using DenseNet-based deep learning model, combines convolutional layers densely connected propagation extract fine-grained temporal dependencies, ensuring accurate classification presence severity. Experimental validation was conducted on real-world AE data collected under controlled non-leak conditions at varying pressure levels. proposed model achieved an exceptional accuracy 99.76%, demonstrating its ability reliably differentiate between normal operation multiple severities. method effectively reduces costs robust performance across diverse operating environments.

Language: Английский

Citations

5
Cross-validation enhanced digital twin driven fault diagnosis methodology for minor faults of subsea production control system DOI
Chao Yang, Baoping Cai, Rui Zhang

et al.

Mechanical Systems and Signal Processing, Journal Year: 2023, Volume and Issue: 204, P. 110813 - 110813

Published: Oct. 2, 2023

Language: Английский

Citations

39
Contrast-Assisted Domain-Specificity-Removal Network for Semi-Supervised Generalization Fault Diagnosis DOI
Qiuyu Song, Xingxing Jiang, Jie Liu

et al.

IEEE Transactions on Neural Networks and Learning Systems, Journal Year: 2024, Volume and Issue: 36(3), P. 5403 - 5416

Published: April 9, 2024

Unknown domain shift caused by the unavailability of target during training phase degrades performance intelligent fault diagnosis models in practical applications. Domain generalization (DG)-based methods have recently emerged to alleviate influence and improve ability toward invisible working conditions. However, most existing studies are conducted on multiple fully labeled source domains. Meanwhile, domain-specific information related variations conditions is often neglected model training. Therefore, order realize reliable based partially domains, this article proposes a contrast-assisted domain-specificity-removal network (CDSRN) extract transferable features from perspective. Concretely, feature removal branch designed disentangle domain-invariant features, thus excavating generalized only domain-invariance dimension. Simultaneously, proxy-contrastive representation enhancement module embedded facilitate class-discriminative domain-discriminative learning, thereby assisting further improvement capability. Experimental confirm effectiveness competitiveness proposed CDSRN semi-supervised diagnosis.

Language: Английский

Citations

9
Fast and stable fault diagnosis method for composite fault of subsea production system DOI
Chao Yang, Baoping Cai,

Xiangdi Kong

et al.

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 226, P. 112373 - 112373

Published: Jan. 18, 2025

Language: Английский

Citations

1
Graph contrastive learning of modeling global-local interactions under hierarchical strategy: Application in anomaly detection DOI

Weiwei Guo,

Yan Wang, Le Zhou

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106871 - 106871

Published: Feb. 1, 2025

Language: Английский

Citations

1
A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown DOI

Chengyan Ding,

Jie Sun, Xiaojian Li

et al.

Journal of Manufacturing Systems, Journal Year: 2023, Volume and Issue: 71, P. 144 - 157

Published: Sept. 19, 2023

Language: Английский

Citations

19
Diagnostic-prognostic framework for assessing the health status of composite structures DOI
Yang Zhang, Maciej Radzieński, Wiesław Ostachowicz

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 278, P. 109461 - 109461

Published: June 7, 2024

Language: Английский

Citations

8
Optical fiber gas sensor with multi-parameter sensing and environmental anti-interference performance DOI

Gaoliang Chen,

Jin Li, Hongmin Zhu

et al.

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 38, P. 100565 - 100565

Published: Jan. 11, 2024

Language: Английский

Citations

7
A knowledge-driven spatial-temporal graph neural network for quality-related fault detection DOI
Lei Guo, Hongbo Shi, Shuai Tan

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 184, P. 1512 - 1524

Published: Feb. 28, 2024

Language: Английский

Citations

7
Information-integration-based optimal coverage path planning of agricultural unmanned systems formations: From theory to practice DOI
Jian Chen, Tao Chen, Yi Cao

et al.

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 40, P. 100617 - 100617

Published: April 21, 2024

Language: Английский

Citations

7