关键词: 量子计算, 量子遗传算法, 轴向柱塞泵, 特征选择

Abstract:

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 genetic algorithm. In this method, chromosomes were coded by quantum bits, and population was updated 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 coefficient. Then, the optimal feature set was selected form original feature set by QGA. Finally, by using 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 experimental results show, compared with common genetic algorithm, QGA can reduce feature dimension more effectively and improve classification accuracy greatly.

Key words: quantum computation, quantum genetic algorithm(QGA), axial piston pump, feature selection