Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10322933 | Expert Systems with Applications | 2011 | 11 Pages |
Abstract
⺠This paper proposes a novel fault diagnosis system to improve the performance of fault diagnosis. ⺠Kernel Fisher discriminant analysis (KFDA) is used in the first step for feature extraction, then Gaussian mixture model (GMM) and k-nearest neighbor (kNN) are applied for fault detection and isolation on the KFDA subspace. ⺠Since the performance of fault diagnosis system would be degraded in the fault detection stage, fault detection and identification are presented in a holistic manner without an intermediate step in the novel system.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhi-Bo Zhu, Zhi-Huan Song,