Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform

By P. Kankar, Satish C. Sharma, S. Harsha. 2011

72 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/d02f8593074ff0d1dbae33743f94f5ff7dd1fda3

How researchers see this paper

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

In Control and Systems Engineering

1. Ruqiang Yan
2. Xuefeng Chen
3. Robert X. Gao
4. Pavan Kumar Kankar
5. Bin Jiang
6. Hongtian Chen
7. Qingbo He
8. Noureddine Zerhouni

In Mechanical Engineering

1. Robert X. Gao
2. Ruqiang Yan
3. Pavan Kumar Kankar
4. Xuefeng Chen

Citations in the corpus

• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, methodology): “used the similar approach for bearing fault diagnosis [60].”
• A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings (2015, background): “…domain, frequency domain, time frequency domain techniques for fault detection in mechanical systems has been investigated by many researchers (Honarvar and Martin, 1997; Engin et al., 1999; Harsha et al., 2006; Wang and Gao, 2003; Peng and Chu, 2004; Kankar et al., 2011a,b; Kankar et al., 2012).”