Deep convolutional neural network based planet bearing fault classification
Top Cited Papers
- 8 February 2019
- journal article
- research article
- Published by Elsevier BV in Computers in Industry
- Vol. 107, 59-66
- https://doi.org/10.1016/j.compind.2019.02.001
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51335006, 51605244)
This publication has 26 references indexed in Scilit:
- Condition monitoring and fault diagnosis of planetary gearboxes: A reviewMeasurement, 2014
- Two new features for condition monitoring and fault diagnosis of planetary gearboxesJournal of Vibration and Control, 2013
- Failure diagnosis using deep belief learning based health state classificationReliability Engineering & System Safety, 2013
- The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applicationsSignal Processing, 2013
- Synchrosqueezed wavelet transforms: An empirical mode decomposition-like toolApplied and Computational Harmonic Analysis, 2011
- Synchrosqueezing-Based Recovery of Instantaneous Frequency from Nonuniform SamplesSIAM Journal on Mathematical Analysis, 2011
- A multidimensional hybrid intelligent method for gear fault diagnosisExpert Systems with Applications, 2010
- A new feature for monitoring the condition of gearboxes in non-stationary operating conditionsMechanical Systems and Signal Processing, 2009
- Fourier Series Analysis of Epicyclic Gearbox VibrationJournal of Vibration and Acoustics, 2001
- Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in positionPattern Recognition, 1982