Fundamental study on detection of plastic-gear-failure signs (synchronization of a nonlinear oscillator to mesh frequency)

The present paper proposes a new method using neural oscillators to filter out the background noise of vibration in meshing plastic gear pairs for detection of failure signs of the gears. Generally, the vibration caused by the tooth-to-tooth meshing of a rotating gear pair contains various frequency components, which are due to amplitude and frequency modulation caused by eccentricity or torque variation. These modulated frequency components on the measured vibration make detection of gear failure difficult; e.g., gear tooth cracks. In this paper, these unnecessary frequency components are eliminated with a feed-forward control system in which neural oscillator’s synchronization property works. Each neural oscillator is designed to tune the natural frequency to a particular one of the components. The properly-designed neural oscillators can follow the change in the driving torque variation autonomously, because of their synchronization property. Moreover, the output phase of the oscillators is set a difference of 180 degrees from the input one, and is included in the original measured response to eliminate the amplitude of the unnecessary components. For this purpose, a simulated acceleration response of meshing gears was firstly constructed in the manner of adding shaft frequency, secondary meshing frequency and their first order sidebands to the regular meshing components (primary meshing frequency). Furthermore, the proposed noise cancellation method was applied to the simulated response, and it is concluded that the effect of gear tooth crack on a change in the tooth stiffness appears conspicuously on the filtered vibration response.

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