A novel approach to Hurst analysis of motor vibration data in aging processes
Determining age in motors and rotating machinery in general is a serious question. Using the vibration signals collected during motor aging is challenging due to nonlinear and non-stationary nature of these signals. This paper presents a study on applicability of Hurst analysis in motor vibration data collected in an artificial aging process, which at first seems to suggest inapplicability of Hurst Exponents in motor age determinations. Conclusions drawn from the straightforward application of Hurst exponent calculation are used to propose a novel method for Hurst analysis of such signals, named Higuchi-Hurst-Hilbert Vibration Decomposition (H³VD), using Higuchi’s algorithm for Hurst exponent calculation and Feldman’s Hilbert Vibration Decomposition overcoming the shortcomings of the original approach and delivering results that were expected based on previous research on frequency domain features of motor vibration in artificial aging processes. Features of this approach have been presented on both Fractional Gaussian noise model and the real vibration data. Non-accelerated bearing aging vibration data was used to demonstrate practical applicability of this new method.