Intellectual Diagnosis for the Performances of Ball Screw System by Using the Unsupervised Deep Learning Method

Abstract

The efficiency of the machine will degrade with the increase in working time It is important to diagnose the working state of the machine This study focuses on intellectual diagnosis for the performance of ball screw system by using an unsupervised deep learning method There are two purposes of this study：first figure out the performance change of ball screw with working time then giving instant lubrication Because the degradation of ball screw system is difficult to figure out by manual methods This study can show the degradation by using a unsupervised deep learning method named：Long Short-Term Memory Encoder Decoder (LSTM-ED) With the data analysis ability of the neural network we can obtain Health Index (HI) of ball screw which shown the degradation while ball screw was working This study quantified the health index with an exponential function We found that the performance declined faster as the accumulated running distance increase Also we decomposed raw data into several Intrinsic Mode Functions (IMFs) by using Empirical Mode Decomposition (EMD) and then trained model with each IMF Finally we can obtain HI of each frequency region The health index has a great ability to display the lubrication status of ball screw We can combine preload torque with health index to explain the overall situation With the result of this study we can compensate for machine lubrication immediately before the damage happened

Date of Award	2020
Original language	English
Supervisor	Jen-Fin Lin (Supervisor)