Presenter(s)
Salah Dauga
Files
Download Project (475 KB)
Description
This poster deals with stator, bearing, and rotor fault detection of compressors for refrigeration. Mathematical modeling of compressors for refrigeration for healthy and stator , bearing, and rotor fault condition are explained. In this poster Artificial Neural Network technique is applied for stator, bearing, and rotor fault detection in compressors for refrigeration. By collecting the simulation data from the mathematical model developed in MATLAB simulink, based on: 1. Frequency. 2. Temperature. 3. vibration. The neural network can precisely detect the faults before any major problem occurs.
Publication Date
4-5-2017
Project Designation
Graduate Research - Graduate
Primary Advisor
Raul E. Ordonez
Primary Advisor's Department
Electrical and Computer Engineering
Keywords
Stander Symposium project
Recommended Citation
"Condition monitoring of Compressors for refrigeration" (2017). Stander Symposium Projects. 917.
https://ecommons.udayton.edu/stander_posters/917