Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: application to ball bearing fault detection

This paper addresses the development of an algorithm that can improve the signal-to-noise ratio (SNR) in inchoate faulty signals. The removal of noise and preservation of fault information components cannot be easily achieved. Many techniques for SNR improvement in healthy signals rely on frequency bands. Such techniques have been proven to be efficient in improving the SRN by filtering out frequency bands (FoFBs). However, these techniques cannot reduce noise and preserve fault information when dealing with inchoate faulty signals. Thus, a feature extraction technique based on statistical parameters, which are free from Gaussian noise, is proposed in this paper. The proposed signal subspace-based approach for SNR improvement in inchoate faulty signals is based on a modified principal component analysis (PCA), in which the optimal subspace is selected via a cumulative percent of variance (CPV) criterion and the test statistic condition of the true information loss, which has the tendency to alleviate the impact of Gaussian and non-Gaussian noise and provides useful time domain analysis for non-stationary signals such as vibration, in which spectral contents vary with respect to time. Furthermore, the modified PCA algorithm is combined with a low-pass filter (LPF) to achieve an optimum balance between noise reduction efficiency and the conservation of inchoate fault information. The proposed PCA-LPF algorithm is compared with different filters under different noise levels to find the most efficient approach in terms of optimizing the trade-off between noise reduction efficiency and precision of inchoate fault information conservation, with the final goal of improving the fault detection capability. Further, the performance of the proposed PCA-LPF algorithm was demonstrated with an experimental study on vibration-based ball bearing fault detection.