Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning

Abstract

Planetary gearbox vibration signals are intricate due to the unique structure, complex kinetics and background noise interference. Moreover, they have time shift invariant feature because of the periodical rotation of the mechanical components. Reliable feature extraction from such signals is the key to success in planetary gearbox fault diagnosis. The recently proposed shift invariant K-means singular value decomposition (SI-K-SVD) dictionary learning method offers a good approach to analyze such signals. This method requires neither a priori knowledge on signals to construct any analytic dictionary, nor a large number of sample signals to form a training dictionary. It is therefore data-driven in nature, and highly flexible and adaptive to represent signals. In this paper, such properties of the SI-K-SVD are exploited to extract the latent constituent components of complex signals and use them to represent signals sparsely, thus suppressing background noise and revealing the true vibration patterns. To apply the SI-K-SVD method to planetary gearbox signals, the criteria for determining the key parameters used in the SI-K-SVD, i.e., the length of pattern and number of decompositions, are suggested. The method has been illustrated by analyzing numerically simulated signals. It has been favorably compared with the frequently used matching pursuit (MP) and K-means singular value decomposition (K-SVD) methods. Its effectiveness in real planetary gearbox fault diagnosis has been validated by analysis of lab experimental signals of a planetary gearbox. The results show that both localized and distributed gear faults can be diagnosed successfully.