Optimal combinations of pattern classifiers
Pattern Recognition Letters
A fuzzy neural network approach to machine condition monitoring
Computers and Industrial Engineering - Special issue: Selected papers from the 25th international conference on computers & industrial engineering in New Orleans, Louisiana
IEEE Transactions on Knowledge and Data Engineering
Voting Algorithm of Fuzzy ARTMAP and Its Application to Fault Diagnosis
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Short-term power load forecasting using grey correlation contest modeling
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
In this paper, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is presented and applied to the fault diagnosis of rolling element bearings. First, by the statistical method, continuous Morlet wavelet analysis method and time series analysis method many features are extracted from the vibration signals to depict the information about the bearings. Second, with the modified distance discriminant technique some salient and sensitive features are selected. Finally, the optimal features are input into a committee of FAMs in different sequence, the output from these FAMs is combined and the combined decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs ensemble can reliably diagnose different fault conditions including different categories and severities, and has a better diagnosis performance compared with single FAM.