Open AccessLinear Bearing Fault Detection Using an Artificial Neural Network Based on a PI Servo System with the Observer for High-speed Automation Machine
Author(s) -
Thanasak Wanglomklang,
Prathan Chommaungpuck,
Jiraphon Srisertpol
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/717/1/012011
Subject(s) - artificial neural network , bearing (navigation) , control theory (sociology) , fault detection and isolation , automation , computer science , servomechanism , control engineering , servo , observer (physics) , artificial intelligence , lubrication , fault (geology) , engineering , control (management) , actuator , mechanical engineering , physics , quantum mechanics , seismology , geology
This research presents the novel approach for linear bearing fault detection by using Artificial Neural Network (ANN) based on observable information for high-speed automation machine. The dynamics modelling of feed drives and servo system design using pole placement technique were established to support the propose method. Three conditions of linear bearings which included healthy, 50 % of lubrication oil and starved lubrication were set up. Feature extraction of the data was analyzed by statistical approach. The results explains clearly that the control system design has a performance for tracking response and the ANN model can achieved 99.7 % accuracy by using the Levenberg Marquardt algorithm.