Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA)

This publication has 15 references indexed in Scilit:

• A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
  Neurocomputing, 2018
• Dynamic modeling of gearbox faults: A review
  Mechanical Systems and Signal Processing, 2018
• A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
  Measurement, 2017
• Detection of Pitting in Gears Using a Deep Sparse Autoencoder
  Applied Sciences, 2017
• Grasshopper Optimisation Algorithm: Theory and application
  Advances in Engineering Software, 2017
• An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
  Sensors, 2017
• Gears-Part I
  American Journal of Engineering and Applied Sciences, 2017
• Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
  Mechanical Systems and Signal Processing, 2016
• Hierarchical feature selection based on relative dependency for gear fault diagnosis
  Applied Intelligence, 2015
• Computerized design, simulation of meshing, and finite element analysis of two types of geometry of curvilinear cylindrical gears
  Computer Methods in Applied Mechanics and Engineering, 2014