CAJ | 학술논문

为了进一步减少特征维数、缩短运算时间、提高分类正确率等，提出了一种基于量子遗传算法的轴向柱塞泵故障特征选择方法，该方法采用量子位进行染色体编码，利用量子门更新种群。首先，对轴向柱塞泵振动信号进行小波包变换，提取出原始信号和各个小波包系数的统计特征；然后，利用量子遗传算法从原始特征集中选择出最优特征集；最后，以神经网络为分类器（其输入为最优特征集），对故障进行诊断与识别。利用该方法对轴向柱塞泵正常、缸体与配流盘磨损和柱塞滑履松动三种状态的特征集进行选择，试验结果表明，与普通遗传算法相比，量子遗传算法可以更有效地减少特征维数，提高分类正确率。
위료진일보감소특정유수、축단운산시간、제고분류정학솔등，제출료일충기우양자유전산법적축향주새빙고장특정선택방법，해방법채용양자위진행염색체편마，이용양자문경신충군。수선，대축향주새빙진동신호진행소파포변환，제취출원시신호화각개소파포계수적통계특정；연후，이용양자유전산법종원시특정집중선택출최우특정집；최후，이신경망락위분류기（기수입위최우특정집），대고장진행진단여식별。이용해방법대축향주새빙정상、항체여배류반마손화주새활리송동삼충상태적특정집진행선택，시험결과표명，여보통유전산법상비，양자유전산법가이경유효지감소특정유수，제고분류정학솔。
In order to reduce feature dimension ,shorten calculation time and improve classification accuracy ,a fault feature selection method for axial piston pump was proposed based on quantum ge-netic algorithm .In this method ,chromosomes were coded by quantum bits ,and population was up-dated with quantum gate .Firstly ,the vibration signals of axial piston pump were decomposed by wavelet transform ,and the statistic features were extracted from original signals and each wavelet co-efficient .Then ,the optimal feature set was selected form original feature set by QGA .Finally ,by u-sing neural network as classifier ,the optimal feature set was used as input for fault diagnosis .This proposed method was used for distinguishing different operating states of axial piston pump .The ex-perimental results show ,compared with common genetic algorithm ,QGA can reduce feature dimen-sion more effectively and improve classification accuracy greatly .