Automobile Independent Fault Detection based on Acoustic Emission Using FFT

Recently, research on effective Acoustic Emission (AE)-based methods for condition monitoring and fault detection has attracted many researchers. Due to the complex properties of acoustic signals, effective features for fault detection cannot be easily extracted from the raw acoustic signals. To solve this problem, Fast Fourier Transform (FFT) is utilized. This method depends on the variations in frequency to distinguish different operating conditions of a machine. In this study, the intension is to categorize the acoustic signals into healthy and faulty classes. Acoustic emission signals are generated from four different automobile engines in both healthy and faulty conditions. The investigated fault is within the ignition system of the engines while they might suffer from other possible problems as well that may affect the generated acoustic signals. The energy of FFT coefficients of acoustic signals for different frequency bands are calculated as representative features. Dimension reduction is performed on the dataset using Principal Component Analysis (PCA) method. The classification accuracy is validated and reported using 10-fold cross validation in which 10 percent of data is randomly selected for training and 90 percent for testing. The classification results are reported to be more than 80%.