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
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
IEEE Transactions on Neural Networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
EuroMed'10 Proceedings of the Third international conference on Digital heritage
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy lattice classifier and its application to bearing fault diagnosis
Applied Soft Computing
Hi-index | 12.05 |
Considering different importance of the feature parameters to the fault conditions of bearing, a modified fuzzy ARTMAP (FAM) network model based on the feature-weight learning is presented in this paper. The features in time-domain, frequency-domain and wavelet-domain are extracted from the vibration signals to characterize the information relevant to the fault conditions of bearing. By the improved distance evaluation technique the optimal features are selected and the corresponding feature-weights which are assigned to the features to indicate their different importance to the fault conditions of bearing are obtained. Then they are combined with the modified FAM which is described by the weighted Manhattan distance and applied to the seven-class fault diagnosis of bearing. To assess the effectiveness and stability of the modified FAM network, bootstrapping method is employed to quantify the stability of the network performance statistically. Diagnosis results show that the modified FAM can more reliably and accurately recognize different fault classes.