Tracking the location of bearing outer raceway defects using multidimensional synchronous signal fusion and tensor Rank-1 decomposition

Abstract

A generalised Warblet tensor rank-1 decomposition (GWTTR1) method is developed in this study to realise location diagnosis for bearing outer raceway defects in low signal-to-noise ratio (SNR) scenarios. Firstly, a novel third-order tensor model (channel–time–frequency) with comprehensive information is established to reveal key information hidden in multidimensional signals and realise feature fusion among two-channel signals. Secondly, the defect characteristic frequency and correlation coefficient between rank-1 tensors are introduced as optimal selection indices for the factor matrix to decompose the tensor model effectively and overcome the inherent shortcomings of decomposition. Thirdly, a novel location diagnosis dimensionless index, namely, the horizontal-vertical synchronisation factor (HVSF), is proposed for the optimised tensor model. Finally, the performance of the proposed GWTTR1 method and novel HVSF index in location diagnosis for bearing outer raceway defects and noise interference elimination is evaluated comprehensively with low SNR dynamic simulation and experimental signals. The comparison results reveal that the GWTTR1 method outperforms existing noise reduction techniques.