Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

Abstract

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults. However, because of heavy background noise and the interferences of other vibrations, it is difficult to extract these impulsive components caused by faults, particularly early faults, from the measured vibration signals. To capture the high-level structure of impulsive components embedded in measured vibration signals, a dictionary learning method called shift-invariant K-means singular value decomposition (SI-K-SVD) dictionary learning is used to detect the early faults of gear-box bearings. Although SI-K-SVD is more flexible and adaptable than existing methods, the improper selection of two SI-K-SVD-related parameters, namely, the number of iterations and the pattern lengths, has an adverse influence on fault detection performance. Therefore, the sparsity of the envelope spectrum (SES) and the kurtosis of the envelope spectrum (KES) are used to select these two key parameters, respectively. SI-K-SVD with the two selected optimal parameter values, referred to as optimal parameter SI-K-SVD (OP-SI-K-SVD), is proposed to detect gear-box bearing faults. The proposed method is verified by both simulations and an experiment. Compared to the state-of-the-art methods, namely, empirical model decomposition, wavelet transform and K-SVD, OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.