Screw performance degradation model based on novel neural networks

Abstract

A screw performance degradation model based on neural network which was optimized by improved genetic algorithm was proposed to predict screw life accurately and provide active maintenance proof. Key factors which related to screw life were analyzed by screw motion mechanism. Three vibration sensors were installed on different position of screw and vibration signal were processed by EMD, time domain analysis, frequency domain analysis and wavelet packet analysis. The most sensitive features to screw life were selected by correlation coefficient and evaluation index. The relation between screw life and features was built by neural network that constructed by BP training algorithm, and screw life was calculated. The long practical results show that the screw life prediction model can meet the need of active maintenance and reduce maintenance cost.