Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning
Zhu Yong-sheng · Xi'an Jiaotong Daxue xuebao · 2011
The case-based reasoning approach is introduced into rolling bearing fault diagnosis.To solve the complexity of feature selection and weights optimization,a Filter/Wrapper integrated feature selection algorithm is proposed.Neighborhood rough set algorithm is applied to select essential features from the feature candidate set,then genetic algorithm is applied to refine the essential feature subset.This method solves the problem of determining the size of neighborhood manually in neighborhood rough set algorithm.Genetic algorithm is also used in feature weights optimization.With the runtime vibration signal of rolling bearing as the basic information,a rolling bearing fault case database is constructed.The historical cases similar to the problem case are recalled and chosen to decide the fault type.The database experiment shows the higher efficiency and accuracy for essential attributes and weights in fault diagnosis.