Pavan Kumar Kankar
Indian Institute of Technology Indore
118 Papers
224 Citations
Pavan Kumar Kankar is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Bearing (mechanical) & Computer science. The author has an hindex of 22, co-authored 98 publications. Previous affiliations of Pavan Kumar Kankar include Indian Institutes of Information Technology & Birla Institute of Technology and Science.
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Papers
Fault diagnosis of ball bearings using machine learning methods
TL;DR: The results show that the machine learning algorithms can be used for automated diagnosis of bearing faults and it is observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.
467
Rolling element bearing fault diagnosis using wavelet transform
TL;DR: The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.
260
A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings
TL;DR: In this paper, the authors proposed permutation entropy as a tool to select best wavelet for feature selection for the detection as well as fault classification of ball bearings, the continuous wavelet coefficients of the time domain signal are calculated at real, positive scales using various real and complex wavelets.
115
A comparison of feature ranking techniques for fault diagnosis of ball bearing
Vinay Vakharia,Vijay Kumar Gupta,Pavan Kumar Kankar +2 more
- 01 Apr 2016
TL;DR: Tenfold cross-validation results show that selected features give enhanced accuracy for detecting faults, and proposed methodology is feasible and effective for fault diagnosis of bearing with reduced feature set.
104
Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier
TL;DR: In this paper, a bearing fault diagnosis method has been proposed based on multi-scale permutation entropy (MPE) and adaptive neuro fuzzy classifier (ANFC), which is applied for feature extraction to reduce the complexity of feature vector.
104