关键词: 丝杠, 性能, 灰色神经网络, 遗传算法, 量子

Abstract: To evaluate the performance of ball screw,the screw performance degradation assessment method based on quantum genetic algorithm and grey neural network was studied.An online monitoring system was designed based on the ball screw of CINCINNATIV5-3000 machining center.The mass data were processed by dynamic clustering data processing technology.Time domain,frequency domain and time-frequency domain features were extracted,and the screw vibration signal feature vectors were constructed by compressing the feature data with principal component analysis method.Initialization parameters of grey neural network were optimized by means of quantum genetic algorithm.The feature vectors were inputted to gray neural network for training,and the screw performance degradation model was obtained.Practice results showed that the screw performance degradation model could effectively evaluate the performance of NC machine screw,while it was with industrial prospects.

Key words: screws, performance, grey neural network, genetic algorithms, quantum