  In this paper artificial neural networks and support vector machines are used
to reduce the amount of vibration data that is required to estimate the Time
Domain Average of a gear vibration signal. Two models for estimating the time
domain average of a gear vibration signal are proposed. The models are tested
on data from an accelerated gear life test rig. Experimental results indicate
that the required data for calculating the Time Domain Average of a gear
vibration signal can be reduced by up to 75% when the proposed models are
implemented.
