Prognostic and Health Management of Ball Screw System by Neural Network

Abstract

In this thesis an innovative fault diagnosis method for Double-nut Ball Screw (DBS) systems is proposed Different from traditional approaches based on accelerometers to examine the health status of ball screw and nuts the methodology employed by this thesis is to use a preload sensor developed by Industry Technology Research Institute (ITRI) instead With the major aim at diagnosing the failure of DBS system correctly and then establishing health management system the combination of signal resampling and Gate Recurrent Uint-II (GRU-II) is established This thesis at first creates a database which collects the failures that often occur in DBS systems and then utilizes signal resampling subsequently to resolve the problems that the time series obtained by preload sensor is inconsistent in sequence and length Following the pre-processing signals GRU-II is manipulated to identify which failures are In contrast to most current diagnosis method GRU-II can achieve great accuracy as well as be insusceptible to noise To evaluate the performance of GRU-II it is set up and verified under Python environment According to simulation results it demonstrates that excellent accuracy can be achieved by the proposed GRU-II either with signal noise or not Compared to the optimal approaches via other neural networks(Feed-forward Long short Term Memory Gate Recurrent Unit) the accuracy of GRU-II is better than those by 13 3% 4 4% and 4 4% respectively Furthermore GRU-II can even maintain recall of recirculating mechanism at 100% As mentioned above the overall prognosis method in this thesis can be potentially applied to the real-world machine tools

Date of Award	2020
Original language	English
Supervisor	Nan-Chyuan Tsai (Supervisor)