B4-FV-19B the oil change Gear unit ventilation Flender FZGs Bevel-helical gear Reduction Boxes B4

Flender/Flender Gear Units/Bevel-helical gear Reduction Boxes B4

ynchronous averaging methodology which compensates for the phase shifting in the measured structural response. Residual signal analysis is technique where non-fault related vibration components are removed from signal so that only those signal components which are indicative of machine faults remain. number of different residual signal methodologies have been investigated in literature. Wang and Wong [7] investigate how an autoregressive (AR) lter which is representative of the baseline vibration signal can be used to detect fault induced signal outliers in novel signal. Heyns et al. [8] extend Wang and Wongs idea to better deal with time-varying operating conditions. This is done by implementing an assemble of weighted AR models, each of which represents the healthy vibration data, but for different operating conditions. When applied to novel signal, the model weights are iteratively updated so as to better account for the instantaneous operating conditions, hence obtaining residual signal which is more sensitive and robust under time-varying operating conditions. They also propose that the structure of the residual signal may be analysed to detect periodic events (outliers), as this may provide insight regarding the nature of the gear damage. The AR lter based residual signal technique is conceptually related to large group of methodologies which are concerned with outlier or novelty detection in data sets. Essentially these outlier or novelty detection methodologies are capable of modelling one class data (.. normal data) and then subsequently detecting samples (.. fault induced) which deviate from the baseline model. Some popular novelty or outlier detection methods include: rule based techniques, neural networks (including multilayer perceptrons, self organizing maps and auto-associative networks), support vector machines, Bayesian networks, nearest neighbour and distance techniques, as well as Gaussian Mixture Models (GMM) density based approaches [9].