Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk

This publication has 46 references indexed in Scilit:

• Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System
  Energies, 2018
• Substructure-based distributed collaborative probabilistic analysis method for low-cycle fatigue damage assessment of turbine blade–disk
  Aerospace Science and Technology, 2018
• The low-cycle fatigue life assessment method for online monitoring of steam turbine rotors
  International Journal of Fatigue, 2018
• A fluid–structure analysis approach and its application in the uncertainty-based multidisciplinary design and optimization for blades
  Advances in Mechanical Engineering, 2018
• Probabilistic fatigue life prediction and reliability assessment of a high pressure turbine disc considering load variations
  International Journal of Damage Mechanics, 2017
• Reliability-based low-cycle fatigue damage analysis for turbine blade with thermo-structural interaction
  Aerospace Science and Technology, 2016
• Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty
  Reliability Engineering & System Safety, 2016
• Multiobject Reliability Analysis of Turbine Blisk with Multidiscipline under Multiphysical Field Interaction
  Advances in Materials Science and Engineering, 2015
• Adaptive explicit decision functions for probabilistic design and optimization using support vector machines
  Computers & Structures, 2008
• Fatigue life prediction in AlMgSi1 lap joint weldments
  International Journal of Fatigue, 2000