A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps

The full vector spectrum is an effective and efficient tool for homologous multi-sensor data fusion in rotating machinery. However, this methodology just takes Fourier Transform to obtain the harmonic trajectory information hidden in multi-sensor data and it has some drawbacks of processing nonlinear, multi-frequency and noise-containing data. To address this critical issue, this paper provides a novel approach called EWT-VCR based on Empirical Wavelet Transform (EWT) and Variance Contribution Rate (VCR) to improve the adaptability and accuracy of the fusion method. EWT is introduced as a signal preprocessing technique to decompose complex signals into variable frequency bands. And VCR is proposed to denoise, fuse EWT components at different frequency bands, and enhance useful harmonic components. The full vector spectrum technology is utilized to carry out the full vector information fusion of the improved multi-sensor signals for further spectrum analysis. The proposed methodology is applied to multi-channel vibration signal fusion for hydraulic pumps to detect specific frequencies related to pump's degradation process and a novel degradation feature named Full Vector Factor Entropy (FVFE) is extracted to describe hydraulic pump's degradation process during its life cycle. The effectiveness of the proposed methods is validated through two experimental cases.