A Relation B-cell Network used for data identification and fault diagnosis

In the current artificial immune algorithms, the process of activation calculation considers the affinity only, but fails to take into account the relationship between sample features. In response to this deficiency, this paper proposes a new Relation B-cell and Relation B-cell Network Model (RBNM), which is basing on the immune mechanism of clone, mutation and network inhibition. In the method, this relation B-cells were calculated by the relationship between sample features. As a result, in the training and diagnosis process, combining with the relation B-cells, the algorithm can effectively improve the ability of cell association monitoring and finally generate efficient detectors. According to the following performance tests, RBNM classification result is not sensitive to its five parameters and results of the method are competitive among comparison methods. Finally, the algorithm was also applied to the reciprocating compressor faults dataset, and the result reached 99.7%. • The proposed method can extract the correlation between features. • RBNM has stable performance and strong robustness within the given parameter range. • The method has good performance in the reciprocating compressor dataset.