FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS

By Lindsay B. Jack, Asoke Kumar Nandi. 2002

399 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7cfa437504805a1be5c29f3c57e208908365f2b6

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. Yaguo Lei
2. Yanyang Zi
3. Tian Han
4. Bo-Suk Yang
5. Achmad Widodo
6. Zhengjia He

In Control and Systems Engineering

1. Yaguo Lei
2. Bo-Suk Yang
3. Achmad Widodo
4. Yanyang Zi
5. Tian Han
6. Shih-Wei Lin
7. Zhengjia He
8. Shih-Chieh Chen

Citations in the corpus

• A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery (2016, background): “The failure modes are normally classified depending on the components that are damaged, differentiating between inner race, outer race, rolling elements and cage failures.(26,79,80) The failure modes of rolling bearings can also be divided into failures caused by excessive operational conditions, by”
• Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors (2007, methodology): “These are some feature selection methods such as conditional entropy (Lehrman, Rechester, & White, 1997), genetic algorithm (GA) (Jack & Nandi, 2002), distance evaluation technique (Yang, Han, & An, 2004; Yang et al., 2005).”
• Kernel Methods and Machine Learning (2014, neutral): “For a few examples, see [29, 35, 74, 89, 97, 103, 109, 113, 173, 184, 188, 211, 245, 256, 272, 273, 318].”
• Parameter determination of support vector machine and feature selection using simulated annealing approach (2008, background): “Jack and Nandi [34] and Shon et al. [35], adopted GA in screening the features of a dataset.”
• A new approach to intelligent fault diagnosis of rotating machinery (2008, methodology): “There are some feature selection methods such as conditional entropy (Lehrman, Rechester, & White, 1997), genetic algorithms (Jack & Nandi, 2002; Samanta, 2004), distance evaluation technique (Yang, Han, & An, 2004; Yang & Kim, 2006), etc. Due to the simpleness and reliability of the distance…”