Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

[1]  Rajan Chattamvelli,et al.  Statistics for Scientists and Engineers , 2015 .

[2]  Jyoti K. Sinha,et al.  Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type , 2015 .

[3]  P. MacConnell,et al.  Crack detection in a rotating shaft using artificial neural networks and PSD characterisation , 2014 .

[4]  Lionel Tarassenko,et al.  Guide to Neural Computing Applications , 1998 .

[5]  James Hensman,et al.  Natural computing for mechanical systems research: A tutorial overview , 2011 .

[6]  Rajan Chattamvelli,et al.  Statistics for Scientists and Engineers: Shanmugam/Statistics for Scientists and Engineers , 2015 .

[7]  Ian K. Jennions,et al.  Unbalance localization through machine nonlinearities using an artificial neural network approach , 2014 .

[8]  Mohsen Esfahanian,et al.  Fault identification in rotating machinery using artificial neural networks , 2005 .

[9]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[10]  Jyoti K. Sinha,et al.  Sensitivity analysis of higher order coherent spectra in machine faults diagnosis , 2016 .

[11]  Jyoti K. Sinha,et al.  Comparison of experimental observations in rotating machines with simple mathematical simulations , 2016 .

[12]  Nalinaksh S. Vyas,et al.  Artificial neural network design for fault identification in a rotor-bearing system , 2001 .