Ball Screw Stability Degradation Stages Evaluation Based on Deep Residual Neural Network and Multi-sensor Fusion

Abstract

This paper proposes a method to evaluate the degradation stages of stability of the screw with data fusion technology and deep residual neural network. Firstly, the data provided by multi-sensors are fused, and then the time domain images of the signals are input into the deep residual neural network for training and testing. The effectiveness of the proposed method is verified using data sets collected from the degradation test bench of ball screw. The data sets contain massive samples involving 3 degradation stages under 9 working conditions obtained by 3 accelerometers. From the results and comparison, it can be found that the deep residual neural network can automatically extract the features from the original data layer by layer and achieve a better evaluation rate when it is used to evaluate the performance stage of the screw, and the multi-sensor data fusion vibration data can better reflect the degradation stages of the screw's performance. It is showed that this method enhances the intelligence of the evaluation compared with the traditional method.