Detección de fricción estática basado en el método de redes neuronales artificiales - Daneshwar, Muhammadamin; Mohamad Kekshar, Sirwan; Aminifar, Sadegh (Innovaciencia, 2019)
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.