Fourier transform and correlation-based feature selection for fault detection of automobile engines

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 extract representative features, signal processing techniques play an important role. One of the commonest techniques is Fast Fourier Transform (FFT). This method depends on the variations in frequency domain 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 features. Correlation-based Feature Selection (CFS) algorithm is used to reduce the dimensionality of the dataset. The case study is carried-out on 4 different types of automobiles using 480 automobiles to prove the independency of the proposed approach on the type of the automobile. Classification results are reported to be around 88 percent accuracy.