Fault diagnosis of ball bearings using continuous wavelet transform

By Pavan Kumar Kankar, Satish C. Sharma, Suraj Prakash Harsha. Applied Soft Computing, 2011

313 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/e79d6657c614b0ed9e804028991cb667b706a225

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, background): “to select appropriate wavelet function from six candidate wavelets for extracting bearing defect-induced features [168].”
• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Kankar et al. (2011a) proposed a fault diagnosis method of ball bearings having localized defects on the various bearing components using wavelet-based feature extraction.”
• 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).”