Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

Язык: Английский

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Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

Reliability Engineering & System Safety, Год журнала: 2024, Номер 245, С. 110037 - 110037

Опубликована: Фев. 24, 2024

Язык: Английский

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MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios

IEEE Internet of Things Journal, Год журнала: 2024, Номер 11(13), С. 24073 - 24082

Опубликована: Апрель 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.

Язык: Английский

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Time series modeling and forecasting with feature decomposition and interaction for prognostics and health management in nuclear power plant

Energy, Год журнала: 2025, Номер unknown, С. 135784 - 135784

Опубликована: Апрель 1, 2025

Язык: Английский

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A novel meta-transfer learning approach via convolutional multi-head self-attention network for few-shot fault diagnosis

Knowledge-Based Systems, Год журнала: 2024, Номер 299, С. 112113 - 112113

Опубликована: Июнь 12, 2024

Язык: Английский

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Source-free domain adaptation for transferable remaining useful life prediction of machine considering source data absence

Reliability Engineering & System Safety, Год журнала: 2024, Номер 246, С. 110079 - 110079

Опубликована: Март 14, 2024

Язык: Английский

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Trustworthy Bayesian Deep Learning Framework for Uncertainty Quantification and Confidence Calibration: Application in Machinery Fault Diagnosis

Reliability Engineering & System Safety, Год журнала: 2024, Номер 255, С. 110657 - 110657

Опубликована: Ноя. 13, 2024

Язык: Английский

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BAKER: Bayesian Kernel Uncertainty in Domain-Specific Document Modelling

Опубликована: Фев. 26, 2025

Язык: Английский

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

Mathematics, Год журнала: 2025, Номер 13(5), С. 797 - 797

Опубликована: Фев. 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

Язык: Английский

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Adaptive signal regime for identifying transient shifts: A novel approach toward fault diagnosis in wind turbine systems

Ocean Engineering, Год журнала: 2025, Номер 325, С. 120798 - 120798

Опубликована: Март 6, 2025

Язык: Английский

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