Detection and classification of mechanical faults of an engine alternator based on vibration signals and frequency analysis

In this article, an intelligent system is introduced in order to detection and classification of some common mechanical faults of an engine alternator based on the frequency analysis of vibration signals. For this purpose, firstly the vibration signals of an alternator under four conditions, including healthy, bearing corrosion, cracked rotor and unbalanced excited shaft, were captured by an accelerometer. Time-domain signals were then transformed into frequency-domain with the aid of FFT. At the next step, power spectral density (PSD) method was used for the secondary frequency signal processing level. Afterward, in data mining step, twelve statistical features were extracted from the PSD values of the signals, which were fed as the input data into the ANN classifier to detect and classify the alternator faults. The results indicate that the proposed method has the capable of detecting the different alternator faults with an accuracy higher than 92%.