A novel physics-guided approach for time-varying mesh stiffness estimation and data generation in gear fault diagnosis under imbalanced data

No abstract is available for this paper.

• Publication year

  2026

• Venue

  Advanced Engineering Informatics

• Publication date

  2026-01-01

• Fields of study

  Physics, Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.aei.2025.103963
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A state of the art in digital twin for intelligent fault diagnosis

  2025cited by this paper
• Model and network dual-driven fault diagnosis framework for canned motor pumps under imbalanced data

  2025cited by this paper
• Physics-informed dual guidance method using physical envelope harmonic distribution and transfer learning for few-shot gear fault classification

  2025cited by this paper
• An enhanced generative adversarial network for longer vibration time data generation under variable operating conditions for imbalanced bearing fault diagnosis

  2025cited by this paper
• Coupled Vibration Model-Driven Intelligent Fault Diagnosis in Canned Motor Pumps

  2025cited by this paper
• Digital twin-assisted fault diagnosis framework for rolling bearings under imbalanced data

  2025cited by this paper
• Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples

  2024cited by this paper
• M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

  2024cited by this paper
• Spectrum-guided GAN with density-directionality sampling: Diverse high-fidelity signal generation for fault diagnosis of rotating machinery

  2024cited by this paper
• Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced samples

  2024cited by this paper
• A data-physic driven method for gear fault diagnosis using PINN and pseudo-dynamic features

  2024cited by this paper
• Inverse physics-informed neural networks for digital twin-based bearing fault diagnosis under imbalanced samples

  2024cited by this paper
• Dynamic Model-Embedded Intelligent Machine Fault Diagnosis Without Fault Data

  2023cited by this paper
• Maximumly weighted iteration for solving inverse problems in dynamics

  2023cited by this paper
• A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis

  2023cited by this paper
• Physics-Guided Generative Adversarial Networks for fault detection of underwater thruster

  2023cited by this paper
• A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

  2023cited by this paper
• Physics informed neural networks for fault severity identification of axial piston pumps

  2023cited by this paper
• Digital twin modeling

  2022cited by this paper
• A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning

  2022cited by this paper
• A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data

  2022cited by this paper
• A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions

  2022cited by this paper
• Multiscale Weighted Morphological Network Based Feature Learning of Vibration Signals for Machinery Fault Diagnosis

  2022cited by this paper
• Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump

  2022cited by this paper
• Physics-informed machine learning

  2021cited by this paper
• A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

  2021cited by this paper
• Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing

  2021cited by this paper
• Dynamical modeling and experimental validation for tooth pitting and spalling in spur gears

  2019cited by this paper
• A survey on Deep Learning based bearing fault diagnosis

  2019cited by this paper
• Systems biology informed deep learning for inferring parameters and hidden dynamics

  2019cited by this paper
• Digital Twin in Industry: State-of-the-Art

  2019cited by this paper
• Squeeze-and-Excitation Networks

  2017cited by this paper
• A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

  2017cited by this paper
• Domain-Adversarial Training of Neural Networks

  2015cited by this paper
• Domain Adaptation via Transfer Component Analysis

  2009cited by this paper

• No citing papers are available for this paper.