Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

By S. Abbasion, Ahmad Rafsanjani, A. Farshidianfar, N. Irani. 2007

408 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8f01a3504c90423f76cae6dd2edcd3d9e5da1601

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Pingfeng Wang
2. Prasanna Tamilselvan

In Control and Systems Engineering

1. Long Zhang
2. Hesheng Liu
3. Weizhong Guo
4. Huijun Zou
5. Pingfeng Wang
6. Prasanna Tamilselvan
7. G. Xiong
8. Haidong Shao

Citations in the corpus

• Nonlinear dynamic modeling of surface defects in rolling element bearing systems (2009, neutral): “[28].”
• A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction (2010, methodology): “To validate and compare the feature extraction ability of various entropies for fault diagnosis, experiments were conducted on rolling element bearing vibration data acquired from Case Western Reverse Lab (Lou and Loparo, 2004; Loparo, 2005; Abbasion et al., 2007 ).”
• Failure diagnosis using deep belief learning based health state classification (2013, methodology): “Significant advances have been achieved in applying classification techniques based on machine learning [27–34] or statistical inferences [35–37], which resulted in a number of state-of-the-art health state classification methods, such as back-propagation neural network (BNN) [27–30], self-organizin”