Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors
By Achmad Widodo, Bo-Suk Yang. Expert systems with applications, 2007
314 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/3792ede75c68b1665417911a8eda18dee2693153
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
- M. Ismail Gursoy
- Abdulhamit Subasi
- Hadi Banaee
- Mobyen Uddin Ahmed
- Amy Loutfi
- Gaoliang Peng
- Yuanhang Chen
- Zhujun Zhang
In Signal Processing
- M. Ismail Gursoy
- Abdulhamit Subasi
- Hadi Banaee
- Mobyen Uddin Ahmed
- Amy Loutfi
- Gaoliang Peng
- Zhujun Zhang
- Yuanhang Chen
Citations in the corpus
- Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine (2009, methodology): “Feature reduction is performed by calculating the eigenvalue of covariance matrix in the component analysis (Widodo et al., 2007; Widodo & Yang, 2007), then the features which correlated to highest eigenvalue are selected as data input for classification.”
- Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, methodology): “Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA [66] which are statistically selecting the subset of the most significant features [45,67].”
- A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “Common dimension reduction methods include principal component analysis (PCA) [9], independent component analysis (ICA) [10], and feature discriminant analysis.”
- EEG signal classification using PCA, ICA, LDA and support vector machines (2010, methodology): “In the past, interpretation of the EEG was limited to visual inspection by a neurophysiologist, an individual trained to qualitatively make a distinction between normal EEG activity and abnormalities contained within EEG records.”
- Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, unknown): “Details are given in [112, 113].”