Intelligent Diagnosis of Rolling Bearing Faults Based on Neural-Fuzzy Network

The demand for different products, higher quality, cost reduction and competition in the industry has led to the expansion of the use of intelligent fault detection systems. Diagnosing rolling bearing defects as one of the main components of electric motors has played an essential role in the reliable performance of production units. In addition, vibration analysis is one of the most powerful tools in detecting faults and machinery condition monitoring. Also, fuzzy neural network can be used in classification problems. This study proposes an intelligent system for fault detection of rolling bearings. In the intelligent fault detection system, the extracted features of vibration signals in the time domain and the adaptive neural-fuzzy system have been used. The train and test datasets are presented to the adaptive neural-fuzzy intelligent system. The simulation results show the performance of the intelligent system for faults diagnosis of rolling bearing to be very successful and acceptable.

کلیدواژه ها:

fault diagnosis ، rolling bearing ، vibration analysis ، adaptive neural-fuzzy system ، feature extraction