An Intelligent Diagnostic Method Based on Optimizing B-cell Pool Clonal Selection Classification Algorithm

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
The trend of intellectualization and complication of mechanical equipment makes the demand for intelligent 4 diagnostic methods more and more intense in industry. In view of the difficulty of obtaining mechanical fault samples and 5 the requirement of clear and reliable diagnosis results, intelligent diagnosis methods need to adapt to the learning of small 6 samples and have the interpretability of white box model. In this paper, inspired by biological immunity, an intelligent 7 fault diagnosis
more » ... t 7 fault diagnosis method was proposed--Optimizing B-cell Pool Clonal Selection Classification Algorithm(OBPCSCA). 8 The OBPCSCA provides a method to construct unique B-cell pools corresponding to specific antigen pools, and uses 9 greedy strategy to generate memory B-cell pools. The experimental comparison with AIRS and AICSL on four UCI 10 benchmark data sets shows that the OBPCSCA has a better balance between the number of memory cells and the 11 accuracy of classification. In particular, compared with AIRS, the OBPCSCA can greatly reduce the number of memory 12 B-cells on the premise of ensuring high classification accuracy. In comparison with the top general classifiers, the 13 OBPCSCA has certain competitiveness in these four data sets. Finally, the algorithm was applied to the bearing data 14 set of Case Western Reserve University for fault diagnosis, and the results showed effectiveness of the algorithm. 15
doi:10.3906/elk-2002-75 fatcat:3qbruyql2za5vn6xgtig5vuxjm