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 to the 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 signal of an alternator under four conditions, including healthy, bearing corrosion, cracked rotor, and the unbalanced excited shaft was captured by an accelerometer. Timedomain signals were then transformed into frequency-domain with the aid of FFT. At the next step, the power spectral density (PSD) method was used for the second frequency signal processing level. Afterward, in the 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 capability of detecting the different alternator faults with an accuracy higher than92%.