Open AccessStatic Friction Detection Based on Artificial Neural Networks Method
Author(s) -
M. A. Daneshwar,
Sirwan Mohamad Kekshar,
Sadegh Aminifar
Publication year - 2019
Publication title -
innovaciencia
Language(s) - English
Resource type - Journals
ISSN - 2346-075X
DOI - 10.15649/2346075x.692
Subject(s) - stiction , process (computing) , artificial neural network , computer science , control theory (sociology) , signal (programming language) , energy (signal processing) , control valves , energy consumption , control (management) , control engineering , artificial intelligence , engineering , materials science , mathematics , microelectromechanical systems , statistics , optoelectronics , electrical engineering , programming language , operating system
Poor product quality and high energy consumption ofmany control loops is due to the presence of static friction. This phenomenon is monitored by human in many industrials. The decision ismade based on human’s brain which is not effective and reliable. Methods: A model-based method of stiction detection based on an artificialneural network (ANN) is proposed. The ANN which is run in parallel tothe process predicts a dynamic model of the process using data obtainedfrom control signal and process output. Results: It can be seen that theproposed method based on ANN can be replaced with human monitoring method. Conclusions: Capability of the proposed method of staticfriction detection for the process with the sticky valve is confirmed bydata obtained from the simulation in a control loop with sticky valve.