Static Friction Detection Based on Artificial Neural Networks Method

Muhammadamin  Daneshwar; Sirwan  Mohamad Kekshar; Sadegh  Aminifar

doi:10.15649/2346075X.692

Autores/as

Muhammadamin Daneshwar Muhammadamin Daneshwar is with the Computer Science Department, Faculty of Science, Soran University
Sirwan Mohamad Kekshar Sirwan Mohamad Kekshar is with the Computer Science Department, Faculty of Science, Soran University
Sadegh Aminifar Sadegh Aminifar is with the Computer Science Department, Faculty of Science, Soran University

DOI:

https://doi.org/10.15649/2346075X.692

Palabras clave:

static friction, model, static friction, neural networks.

Resumen

Introduction: Poor product quality and high energy consumption of
many control loops is due to the presence of static friction. This phenomenon is monitored by human in many industrials. The decision is
made based on human’s brain which is not effective and reliable. Methods: A model-based method of stiction detection based on an artificial
neural network (ANN) is proposed. The ANN which is run in parallel to
the process predicts a dynamic model of the process using data obtained
from control signal and process output. Results: It can be seen that the
proposed method based on ANN can be replaced with human monitoring method. Conclusions: Capability of the proposed method of static
friction detection for the process with the sticky valve is confirmed by
data obtained from the simulation in a control loop with sticky valve.

Biografía del autor/a

Muhammadamin Daneshwar, Muhammadamin Daneshwar is with the Computer Science Department, Faculty of Science, Soran University

Muhammadamin Daneshwar is with the Computer Science Department, Faculty of Science, Soran University

Sirwan Mohamad Kekshar, Sirwan Mohamad Kekshar is with the Computer Science Department, Faculty of Science, Soran University

Sirwan Mohamad Kekshar is with the Computer Science Department, Faculty of Science, Soran University

Sadegh Aminifar, Sadegh Aminifar is with the Computer Science Department, Faculty of Science, Soran University

Sadegh Aminifar is with the Computer Science Department, Faculty of Science, Soran University

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