Author/Authors :
Maghsoudi Gharehbolagh، Ghasem نويسنده Department of Mechanical Engineering, Eslamshahr Branch, Islamic Azad University, Eslamshahr, Tehran, Iran , , Farokhzad، Saeid نويسنده Ph.D Student of Mechanical Engineering of Agricultural Machinery, University of Urmia, Urmia, Iran , , Asadi Asad Abad، Mohammad Reza نويسنده Department of Mechanical Engineering, Buinzahra branch, Islamic Azad University, Buinzahra, Iran , , Ranjbarkohan، Mohammad نويسنده Department of Mechanical Engineering, Buinzahra branch, Islamic Azad University, Buinzahra, Iran ,
Abstract :
This paper presented an adaptive network fuzzy inference system (ANFIS) to diagnose the fault type of the gearbox. The gearbox conditions to be considered were healthy, broken gear, worn gear and worn bearing. These features are extracted from vibration signals using the FFT technique. The features were fed into an adaptive neuro-fuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 95.24%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.
