Fault Recognition Method Based On Neighborhood Structure Analysis Of Feature Space

Fault diagnosis plays a crucial role in improving the reliability of equipments. Faultdiagnosis users expect that the fault location, fault classes and the trend could be effec-tively and rapidly judged. Meanwhile, the instantaneity, online update ability and accu-racy are essential. Fault analysis based on knowledge discovery is one of the most effec-tive ways. In view of the constraint of the stability and the accuracy for the present faultrecognition module in practical application, in this paper, from the angle of explorationand usage of fault feature subspace, multiple reducts that keep the approximation abilityof original feature space can be obtained, and then information in different reducts can befused through optimization methods to improve generalization capability and stability offault recognition. The main contributions of the work are listed as follows:Firstly, attribute reduction based on neighborhood discernibility matrix and a fastmethod based on sample pair selection are put forward. Attribute reduction based on dis-cernibility matrix is introduced into neighborhood rough set. As to the method based ondiscernibility matrix, only the minimum elements in the matrix are useful for attributereduction. Hence, reducts can be found by looking for minimum elements. Besides, theimpact of neighborhood size on attribute reduction is analyzed. Finally, test the effective-ness of reduct evaluation indexes.Secondly, the method finding all reducts and randomized reduction based on neigh-borhood attribute dependency are constructed. Neighborhood attribute dependence hasalready been used to construct the algorithm looking for a single reduct. In fact, powersets can be used to find all reducts based on attribute dependency. To find a fast and effec-tive methods finding multiple reducts, neighborhood randomized reduction is proposed.The two attribute reduction methods are not only applicable to neighborhood rough set,but can be expanded to classic rough sets and fuzzy rough sets.Thirdly, ensemble learning method based on margin distribution entropy is proposed.In this paper, the concept of margin distribution entropy is put forward to indicate the uni-formity degree of margin distribution. To maximize margin and simultaneously maximizemargin distribution entropy, an ensemble learning method is designed. The classificationperformance and the variation of margin distribution are examined. Fourth, an ensemble learning method based on margin distribution optimization andregularization is proposed. Multiple Classifiers fusion in essence is a special classifi-cation problem. From the angle of classifier design to solve the problem of ensemblelearning is the main idea of this paper. By combining fusion loss minimization and regu-larized learning together, square loss, logistic loss and linear loss are respectively used toconstruct regularization based ensemble learning methods. The boundary of generationability for square loss is given. The classification performance, variation of margin distri-bution and learned weights as to different optimization objectives are tested to prove theeffectiveness of the proposed method.Finally, due to the poor stability and generation ability of the discrimination functionlearned from a single fault feature subspace , by fusing the information in different fea-ture subspaces, it can greatly enhance the stability and accuracy of the fault recognition.Different feature subspaces are obtained through the neighborhood randomized reductionand then they are integrated by margin distribution optimization. The effectiveness of thismethod is tested in gears crack level recognition.In this paper, attribution reduction methods based on neighborhood rough set areunified. Due to the limitation of generalization performance and stability for minimalneighborhood separable subspace, neighborhood reducts ensemble leaning based on mar-gin distribution optimization is constructed. The margin distribution entropy is proposedand optimized to change margin distribution. By combining fusion loss minimization andregularized learning, ensemble learning methods based on three loss functions, differentregularization items and constraints are put forward.