A predictive model for centerline temperature in electrical cabinet fires DOI
Qiuju Ma, Zhennan Chen, Jianhua Chen

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 211, P. 115303 - 115303

Published: Dec. 31, 2024

Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning DOI
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Language: Английский

Citations

10
Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems DOI
Rui Wu, Chao Liu, Dongxiang Jiang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 245, P. 110037 - 110037

Published: Feb. 24, 2024

Language: Английский

Citations

9
MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios DOI
Bo Zhao, Qiqiang Wu, Ke Zhao

et al.

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(13), P. 24073 - 24082

Published: April 16, 2024

As a classical and crucial component in industrial systems, the manipulators are widely employed precision manufacturing scenarios because of their advantages high stiffness, large load support capability, precision. During service, it is inevitable that they encounter data imbalance due to occasional low-frequency failure behaviors. But order address these issues, majority approaches already use need assistance extra tools. Thus, novel intelligent health state diagnosis model, named multiple neighbor homogeneous property-embedded graph auto-encoder (MNHP-GAE), developed get around this restriction apply manipulators. Its core realize expansion enrichment feature space by mining effective complementary information from property samples without augmentation other technologies. Specifically, wavelet decomposition reconstruction dynamic time warping integrated promote quantification sample similarity enable construction samples. Following that, unique module with multi-head attention mechanism constructed extract nodes match for diagnostic tasks. Finally, through multi-case experimental validation scenario 3-PRR planar parallel manipulator platform, superior performances proposed MNHP-GAE model highly unbalanced fully demonstrated.

Language: Английский

Citations

9
Time series modeling and forecasting with feature decomposition and interaction for prognostics and health management in nuclear power plant DOI
Hai-Bo Yu,

Ling Chang,

Minghan Yang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135784 - 135784

Published: April 1, 2025

Language: Английский

Citations

1
A novel meta-transfer learning approach via convolutional multi-head self-attention network for few-shot fault diagnosis DOI
Lanjun Wan, Le Huang, Jiaen Ning

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 299, P. 112113 - 112113

Published: June 12, 2024

Language: Английский

Citations

6
Source-free domain adaptation for transferable remaining useful life prediction of machine considering source data absence DOI
Yudong Cao, Jichao Zhuang, Qiuhua Miao

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 246, P. 110079 - 110079

Published: March 14, 2024

Language: Английский

Citations

5
Trustworthy Bayesian Deep Learning Framework for Uncertainty Quantification and Confidence Calibration: Application in Machinery Fault Diagnosis DOI
Hao Li, Jinyang Jiao, Zongyang Liu

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 255, P. 110657 - 110657

Published: Nov. 13, 2024

Language: Английский

Citations

4
BAKER: Bayesian Kernel Uncertainty in Domain-Specific Document Modelling DOI
Ubaid Azam, Imran Razzak, Shelly Vishwakarma

et al.

Published: Feb. 26, 2025

Language: Английский

Citations

0
A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples DOI Creative Commons

Jiasheng Yan,

Yang Sui,

Tao Dai

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(5), P. 797 - 797

Published: Feb. 27, 2025

Intelligent fault diagnosis (IFD) plays a crucial role in reducing maintenance costs and enhancing the reliability of safety-critical energy systems (SCESs). In recent years, deep learning-based IFD methods have achieved high accuracy extracting implicit higher-order correlations between features. However, excessive long training time learning models conflicts with requirements real-time analysis for IFD, hindering their further application practical industrial environments. To address aforementioned challenge, this paper proposes an innovative method SCES that combines particle swarm optimization (PSO) algorithm ensemble broad system (EBLS). Specifically, (BLS), known its low complexity classification accuracy, is adopted as alternative to SCES. Furthermore, EBLS designed enhance model stability high-dimensional small samples by incorporating random forest (RF) strategy into traditional BLS framework. order reduce computational cost EBLS, which constrained selection hyperparameters, PSO employed optimize hyperparameters EBLS. Finally, validated through simulated data from complex nuclear power plant (NPP). Numerical experiments reveal proposed significantly improved diagnostic efficiency while maintaining accuracy. summary, approach shows great promise boosting capabilities

Language: Английский

Citations

0
Adaptive signal regime for identifying transient shifts: A novel approach toward fault diagnosis in wind turbine systems DOI
Peng Chen, Y. Wu, Shuai Fan

et al.

Ocean Engineering, Journal Year: 2025, Volume and Issue: 325, P. 120798 - 120798

Published: March 6, 2025

Language: Английский

Citations

0