CAJ | 학술논문

针对旋转机械复合故障的不确定性和模糊性，在蚁群神经网络的基础上，引入并行机制改进算法。利用多线程技术增大蚁群的搜索区域，同时采用编码映射匹配法则（EMM）提高匹配效率，缩短蚁群寻路时间，加快算法收敛速率，并对BP神经网络进行优化，结合概率转化（BPA）辅助决策。计算结果表明，合成器对复合故障识别率高，与人类决策一致，对其他模拟进化算法有借鉴意义。
침대선전궤계복합고장적불학정성화모호성，재의군신경망락적기출상，인입병행궤제개진산법。이용다선정기술증대의군적수색구역，동시채용편마영사필배법칙（EMM）제고필배효솔，축단의군심로시간，가쾌산법수렴속솔，병대BP신경망락진행우화，결합개솔전화（BPA）보조결책。계산결과표명，합성기대복합고장식별솔고，여인류결책일치，대기타모의진화산법유차감의의。
Considering the uncertainty and fuzziness of compound fault of rotating machinery,ant colony neural network algo-rithm is modified by the introduction of parallel reasoning algorithm in this article.The region of search of the hive is enhanced by multithreading technology,and the application of EMMimproves the matching efficiency to a certain degree.Meanwhile,the time of finding the route is obviously shortened,thus accelerating the rate of convergence.Moreover,BP neural network is also opti-mized.As for aid decision making,BPA is applied.As the result shows,synthesizer learns pretty fast and combined failures can easily be detected,which seems to be in line with human decision-making.Consequently,the modification provides a reference for other evolutionary algorithm simulations.