基于改进人工蜂群算法的多值属性系统故障诊断策略

针对实际工程应用中常见的多值属性系统故障诊断策略问题,在传统人工蜂群算法(Artificial Bee ColonyAlgorithm,简称 ABC算法)的基础上,提出改进蜂群算法用于多值属性系统的测试序列寻优。首先,在蜂群算法中重新定义多值 D矩阵和五元组的含义；其次,引入方向信息概率矩阵,设置状态转移规则和矩阵元素更新策略；最后,采用导弹舵系统实例说明算法的实现过程和有效性。最终测试序列寻优结果表明:与传统的多值 Rollout算法相比,文中所提算法能得到较好的诊断策略结果,具有一定的应用价值。

Fault Diagnosis Strategy of Multi-Valued Attribute System Based on Improved Artificial Bee Colony Algorithm

Aiming at the common fault diagnosis strategy problems of multi-valued attribute systems in practical engineeringapplications, based on the traditional artificial bee colony algorithm, an improved bee colony algorithm is proposed to opti.mize the test sequence of multi-valued attribute systems. Firstly, the meaning of the multi-valued D matrix and the five-tu.ple in the bee colony algorithm is redefined; secondly, the direction information probability matrix is introduced, the statetransition rules and matrix element update strategies are set; finally, the missile rudder system example is used to illustratethe implementation process and the effectiveness of the algorithm. The final test sequence optimization results show that thealgorithm proposed can obtain better diagnostic strategy results compared with the traditional multi-valued Rollout algo.rithm and has a certain application value.