Gear Health Monitoring and Prognosis

A stochastic collocation approach for efficient integrated gear health prognosis

Uncertainty quantification in damage growth is critical in equipment health prognosis and condition based maintenance. Integrated health prognostics has recently drawn growing attention due to its capability to produce more accurate predictions through integrating physical models and real-time condition monitoring data. In the existing literature, simulation is commonly used to account for the ...

Machine Learning for Detecting and Locating Damage in a Rotating Gear

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finitedifference solution of elastodyn...

The source of Acoustic Emission during meshing of spur gears

Condition monitoring of gears with vibration analysis and Spectrometric Oil Analysis Programs (SOAP) are widely accepted and used in the military aviation industry. Whilst the literature to date has shown the application of the Acoustic Emission (AE) technique to gearbox health monitoring still be in its infancy, there exist opportunities to develop the technique into a complementary diagnostic...

Vibration Health Monitoring of Gears

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

With integrated equipment health prognosis, both physical models and condition monitoring data are utilized to achieve more accurate prediction of equipment remaining useful life (RUL). In this paper, an integrated prognostics method is proposed to account for two important factors which were not considered before, the uncertainty in crack initiation time (CIT) and the shock in the degradation....