The study of the on-line fault diagnosis method for induction motor bearing based on AR Model Parameters Identification
- DOI
- 10.2991/asei-15.2015.38How to use a DOI?
- Keywords
- AR model parameters; Identification; Motor bearing fault; On-line diagnosis
- Abstract
To realize the online fault diagnosis for induction motor bearings, a method of online identification based on the AR model parameter recursive identification for induction motors vibration signal was proposed. Firstly, based on the optimal instrumental variable method, four auto-regression models were established for the vibration signals of induction motor under four conditions: the normal condition, out-race fault, inner-race fault and the ball bearing fault. Then, the state equations for the induction motor vibration signals were established by taking the autoregressive model coefficients as the state variables and the AR model coefficients in normal conditions are initial values. The online parameter identification is finished with Kalman filtering technique. Based on these, the relationship of the model coefficients under the four conditions was analyzed by making use of Hierarchical Cluster Analysis, and the online diagnosis for bearing faults in induction motors was accomplished with the Bullock distance of model parameters as criterion. Finally, the feasibility and the effectiveness of the proposed method are proved through the analysis of examples.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ju-mei Yuan AU - Lu Zhao PY - 2015/05 DA - 2015/05 TI - The study of the on-line fault diagnosis method for induction motor bearing based on AR Model Parameters Identification BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 175 EP - 181 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.38 DO - 10.2991/asei-15.2015.38 ID - Yuan2015/05 ER -