Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

By Achmad Widodo, Bo-Suk Yang, Tian Han. Expert systems with applications, 2007

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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.”
A novel method for machine performance degradation assessment based on fixed cycle features test (2009, methodology): “Some examples using the data under the fixed working load can be found in the literature such as ball bearing race fault detection [10], faults diagnosis for induction motor [11] and chiller faults diagnosis [12] and so on.”
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference (2009, unknown): “Different approaches for motors incipient fault detection and diagnosis have successfully been proposed (Yang and Kim, 2006; Satish and Samar, 2005; Yang et al., 2004; Casimir et al., 2006; Widodo et al., 2007; Benbouzid and Nejjari, 2001).”