Synthesized fault diagnosis method reasoned from rough set‐neural network and evidence theory

Abstract

Summary When traditional machinery fault diagnosis methods are used to handle diagnostic problems, the problems such as low diagnosis accuracy and bad real‐time capability will arise if there are lots of data and various complex faults. An integrated fault diagnosis reasoning strategy based on fusing rough sets, neural network, and evidence theory is presented using the principles of data fusion and meta‐synthesis. Firstly, use the the parallel neural network structure to improve diagnosis ability of the local diagnosis networks; preprocess the data with rough set theory to simplify the complex neural networks; and eliminate redundant properties in order to determine the topological structure of network. By this way, the shortcomings of network, such as large scale and slow classification, can be overcome. Secondly, a new objectified method of basic probability assignment is given. Besides, the accuracy and efficiency of the fault diagnosis can be improved obviously according to the various redundant and complementary fault information by using the combination rule of the evidence theory to synthesize and make decisions on the evidence. The example of rotating machinery diagnostic given in the paper proves the method to be feasible and available.

The Comprehensive Diagnostic Method Combining Rough Sets and Evidence Theory

“…With the completion of the reduction algorithm considered, attribute reduction is applied in this paper by combining discernibility matrix, dependability of attribute and the heuristic reduction algorithm of information entropy by improving the importance of attribute. The detailed process of this algorithm is referred in the literature [18,19].…”

New Variable Precision Reduction Algorithm for Decision Tables

“…Attribute reduction is an important task in rough sets [1,2], which were proposed by Pawlak in the 1980s and are used to handle problems involving uncertainty. In recent years, the research on rough sets has combined rough sets with fuzzy sets [3][4][5], evidence theory [6,7], information entropy [8][9][10][11], and other fields, and has made great progress. Attribute reduction removes redundant features and retains the subset with the minimum number of attributes to improve the efficiency of the algorithm.…”

New Variable Precision Reduction Algorithm for Decision Tables

“…Attribute reduction is an important task in rough sets [1] which were proposed by Pawlak in the 1980s and are used to handle problems involving uncertainty [2]. In recent years, the research on rough sets has combined rough sets with fuzzy sets [3], [4], [5] evidence theory [6], [7] information entropy [8], [9], [10], [11] and other fields, and has made great progress. Attribute reduction removes redundant features and retains the subset with the minimum number of attributes to improve the efficiency of the algorithm.…”