Gearbox damage identification using Ensemble Empirical Decomposition method

Abstract

In this article, we have conducted a comparative analysis of vibration signals from helicopter aircraft propulsion transmissions, registered on an industrial research stand. We compared acceleration vibrations in the case of gears without physical damage and gears with one tooth missing. Based on recorded signals, we determined the values of indicators based on the statistical properties of signals and compared them with each other. For a more exact comparison, the distribution of the tested signals to the empirical modes using the EEMD (Ensemble Empirical Mode Decomposition) method was performed. This allows to treat individual modes as components of a signal at specific frequencies, and also prevents mixing of modes in individual components, which may take place in the classic EMD analysis. It should be noted that individual modes may correspond to characteristic frequencies for the operation of the transmission. When comparing the values of the most frequently used indicators, modes/frequencies in which the damage was most visible were indicated.

Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

“…In the next stage, 49 damage/deterioration indices (DIs) are delineated based on computed coefficients and residuals of the AR model using statistical indices listed in Table A of the appendix. Besides, the majority of these indices are prevailing amongst researchers concerning damage identification purposes [42][43][44].…”