Abstract
There is no established threshold or limit for gear vibration based condition indicators (CI) that indicates when a gear is in need of maintenance. The best we can do is set CI thresholds statistically, based on some small probability of false alarm. Further, to the best of our knowledge, there is no single CI that is sensitive to every failure mode of a gear. This suggests that any condition based maintenance system for gears will have some form of sensor fusion.Three statistical models were developed to define a gear health indicator (HI) as a function of CI: order statistics (max of n CIs), sum of CIs and normalized energy. Since CIs tend to be correlated, a whitening process was developed to ensure the HI threshold is consistent with a defined probability of false alarm. These models were developed for CIs with Gaussian or Rayleigh (skewed) distributions. Finally, these functions, used to generate HIs, were tested on gear test stand data and their performance evaluated as compared to the end state of the gear (e.g. photos of damage). Results show the HIs performed well detecting pitting damage to gears.
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