Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection

By Biswanath Samanta, K. R. Al-Balushi, S. Al-Araimi. 2003

525 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b6a53cb84fa4e7357d318f7ef635da3ebd806b5b

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. Shih-Wei Lin
2. Shih-Chieh Chen
3. Tsung-Yuan Tseng
4. Zne-Jung Lee
5. Abdulhamit Subasi
6. Bing Xue
7. Mengjie Zhang
8. Will N. Browne

Citations in the corpus

• Parameter determination of support vector machine and feature selection using simulated annealing approach (2008, methodology): “[36] developed a GA approach to change the RBF width parameter of SVM with feature selection.”
• Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings (2017, unknown): “Each of these bins was further processed to extract the following features from the original, derivative, and integral signals [39, 40]: maximum amplitude, root mean square (RMS), peak-to-peak amplitude, crest factor, arithmetic mean, variance ( 𝜎 2 ), skewness (normalized 3rd central moment),…”