Title :
Fault Prediction Based on Data-Driven Technique
Author :
Luhui, Lin ; Jie, Ma
Author_Institution :
Dept. of Autom., Beijing Inf. Sci. & Technol. Univ., Beijing, China
Abstract :
This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain.
Keywords :
data analysis; feature extraction; principal component analysis; systems analysis; PCA; data-driven technique; fault diagnosis; fault prediction; feature extraction; principal component analysis; system information extraction; Artificial neural networks; Data models; Fault diagnosis; Mathematical model; Monitoring; Principal component analysis; data-driven; fault prediction; improvement; principal component analysis (PCA);
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.253