一种新型免疫网络学习算法在故障诊断中的应用

Application of a novel immune network learning algorithm to fault diagnosis

针对免疫算法对旋转机械故障进行诊断时存在多样性、去冗余方面的困难,提出了一种新型免疫网络学习算法.该算法首次在抗体初始化过程引入了抗体抑制机制,定义了邻近抗体对本抗体的抑制阈.消除了冗余的抗体,增强了抗体的多样性.另外该算法定义了新的学习速率,使得抗体向抗原的方向搜索速度更快.最后将该算法运用在旋转机械故障诊断中,试验结果表明算法能有效地对5种典型故障进行分类识别.

Immune algorithms have problems diagnosing faults in rotating machines. This is due to the volume of unique data points they must process, and the difficulty in eliminating redundant data. Hence, a novel immune network learning algorithm was formulated, in which antibody suppression was introduced in the process of generating initial antibodies, and a suppression threshold for antibodies with respect to neighboring antibodies was defined. Redundant antibodies were eliminated, while allowing the diversity of antibodies to be enhanced. In addition, a new learning rate was defined, increasing the speed antibodies search in the direction of antigens. Finally, the algorithm was tested in fault diagnosis for rotating machines. Experimental results indicated that this algorithm can effectively classify and recognize five typical kinds of faults.