H3-VV-7-A flender graffenstaden france Helical gear box H3

Flender/Flender Gear Units/Helical gear box H3

conventional vibration signal synchronous averaging and spectral analysis. 2. Methodology This paper proposes methodology which combines concepts from residual analysis, novelty detection and syn- chronous averaging. The methodology comprise four key steps as illustrated in gure 1. resample signalcompute NLL discrepancy signalcut signal into periodic segmentscompute NLL synchronous averageFigure 1: Diagram of the four key steps of the proposed NLL discrepancy signal synchronous averaging methodology. 1. The vibration signal is resampled at constant gear angular increments. 2. The resampled signal is segmented into block windows of xed length. The signal segments are synchronous with specic shaft angular intervals. window length may be chosen for instance to represent the (approximate) duration for which gear-pinion tooth pair is in mesh. 3. The negative log likelihood (NLL) of each signal segment is computed. The NLL discrepancy signal is used as measure of the extent to which individual signal segments deviate from the expected healthy behaviour. 4. All the NLL values which correspond to any particular gear (or pinion) angular interval are ensemble averaged. Gear angular intervals with small averaged NLL values correspond well to the expected behaviour, while large averaged NLL values indicate the possible presence of damage. Novelty detection with Gaussian Mixture Models The methodology implemented in this paper makes use of Gaussian mixture model (GMM) density distributions to compute the negative log likelihoods (NLL) of short signal segments. linear combination of Gaussians can give rise to complex multivariate probability densities. Almost any continuous density function may be approximated to arbitrary precision by implementing sufciently many Gaussian components with appropriately selected means, covariances and mixing coefcients [1, 1]. GMM density estimates may thus be well suited to represent the possibly non-linear covariances or multiple density m