Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference
An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance.
Bai
MR
Department of Mechanical Engineering, Chiao-Tung University, Taiwan, Republic of China. msbai@cc.nctu.edu.tw
Hsiao
I
Tsai
H
Lin
C