Fault Diagnosis of Rotating Machinery Based on Integration of Generalized Rough Sets and Neural Networks
Dongxin Xue · Mechanical Science and Technology · 2003
In engineering applications, the incompleteness and redundancy in rules of fault diagnosis often lead to inconvenience. In this paper, rough sets theory was applied to reduction of incomplete diagnosis decision system of rotating machinery to find necessary conditions for diagnosis, and neural networks were used for fault pattern classification. Generalized rough sets theory and its application to reduction of incomplete decision system were introduced. Based on this theory, the incomplete fault diagnosis decision systems of rotating machinery were studied, and the optimal diagnosis rules were obtained. The application of the reduced diagnosis decision system to the neural fault classifier indicated that rough-sets-based-reduction reduces the dimension of input to neural network, and raises the efficiency of training. The practical examples validated the application of generalized rough sets integrated with neural networks to vibration fault diagnosis of rotating machinery.