Condition Monitoring and Fault Diagnosing of Vehicle Electric Alternator using Vibration Signals and Adaptive Neuro Fuzzy Inference System

Electrical Generator has a vital and important role in vehicles, especially the vehicles with Multiplex data transfer system. Developing electric malfunctions can cause catastrophic damages to other electric and electronic systems. Therefore alternator fault detection and monitoring has a significant role to avoid developing faults in other systems. In this research alternator fault detection and monitoring has been done with data extracted from vibration signals using Adaptive Neuro Fuzzy Inference System (ANFIS). To accomplish this task, certain faults are made on the alternator deliberately. Then vibrations from each specific fault are gathered and stored for subsequent analysis. The faults consist of: one phase disconnection, disconnection of positive voltage of regulator, burning of one and two diodes of rectifier set. The vibration signals of healthy alternator as well as different faulty states are gathered from two piezoelectric sensors mounted on alternator body for 30 seconds and 1000, 1500, and 2000 motor RPM. For analyzing vibration signals wavelet packet decomposition in level one was used. The mother wavelet with maximum energy to Shannon entropy was selected as the best choice. First and second energy bands were computed and used as the feature vector to the designed ANFIS. Results shows the proposed ANFIS model was effective and it could predict different faults with perfect match.