Due to increasing requirements regarding the vibrational behavior of automotive
transmissions, it is necessary to develop reliable methods for noise evaluation and
design optimization. Continuous research led to the development of an elaborate method for gear noise evaluation. The presented methodology enables the gear engineer to optimize the microgeometry with respect to robust manufacturing.
Helical gear teeth are affected by cratering wear — particularly in the regions of low oil film thicknesses,
high flank pressures and high sliding speeds. The greatest wear occurs on the pinion — in the area of
negative specific sliding. Here the tooth tip radius of the driven gear makes contact with the flank of the
driving gear with maximum sliding speed and pressure.
This paper proposes a new method — using neural oscillators — for filtering out background vibration noise in meshing plastic gear pairs in the detection of signs of gear failure. In this paper these unnecessary frequency components are eliminated with a feed-forward control system in which the neural oscillator’s synchronization property works. Each neural oscillator is designed to tune the natural frequency to a particular one of the components.