Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising
Author:
- S.K. LahaSLS.K. Laha
- 1 March 2017
- journal article
- Published by Elsevier in Measurement
- Vol. 100, 157-163
Abstract
No abstract availableFunding Information
- Department of Science & Technology, New Delhi, India (SR/FTP/ETA-0040/2011)
This publication has 16 references indexed in Scilit:
- Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosisMechanical Systems and Signal Processing, 2012
- An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosisMeasurement, 2012
- Non-local Methods with Shape-Adaptive Patches (NLM-SAP)Journal of Mathematical Imaging and Vision, 2011
- Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosisMechanical Systems and Signal Processing, 2011
- SURE-Based Non-Local MeansIEEE Signal Processing Letters, 2009
- The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosisMechanical Systems and Signal Processing, 2007
- Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transformMechanical Systems and Signal Processing, 2007
- The spectral kurtosis: a useful tool for characterising non-stationary signalsMechanical Systems and Signal Processing, 2006
- The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machinesMechanical Systems and Signal Processing, 2006
- A Review of Image Denoising Algorithms, with a New OneMultiscale Modeling & Simulation, 2005