Vibration fault diagnosis based on stochastic configuration neural networks

This publication has 30 references indexed in Scilit:

• Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
  Mechanical Systems and Signal Processing, 2016
• Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints
  IEEE Transactions on Neural Networks and Learning Systems, 2015
• Investigation of Heat Treatment of Gears Using a Simultaneous Dual Frequency Induction Heating Method
  IEEE Transactions on Magnetics, 2015
• Gear fault diagnosis using active noise cancellation and adaptive wavelet transform
  Measurement, 2014
• An adaptive morphological gradient lifting wavelet for detecting bearing defects
  Mechanical Systems and Signal Processing, 2012
• Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques
  Mechanical Systems and Signal Processing, 2011
• Fault diagnosis of ball bearings using continuous wavelet transform
  Applied Soft Computing, 2011
• Learning Deep Architectures for AI
  Foundations and Trends in Machine Learning, 2009
• DETECTION OF GEAR DETERIORATION UNDER VARYING LOAD CONDITIONS BY USING THE INSTANTANEOUS POWER SPECTRUM
  Mechanical Systems and Signal Processing, 2000
• Objective functions for training new hidden units in constructive neural networks
  IEEE Transactions on Neural Networks, 1997