Hybrid Diagnostics and Prognostics of Planetary Gearboxes

Abstract

The effective condition monitoring of planetary gearboxes is important for making informed maintenance decisions and minimizing machine downtime. Traditional physics-based and data-driven approaches for condition monitoring of planetary gearboxes have inherent shortcomings that limit their practical application for remaining useful life prediction. Hybrid approaches show unique potential for performing diagnostics and prognostics of planetary gearboxes in situations where failure data is not available. Hybrid methods incorporate both physics-based and data-driven models that exploit their respective advantages. This work starts with a brief overview of the various challenges associated with vibration-based condition monitoring of planetary gearboxes and the characteristics of planetary gearboxes that complicate the condition monitoring process. Thereafter, the potential of hybrid methods to address these challenges is discussed. The respective advantages and disadvantages of physics-based and data-driven approaches are summarized and a short review of the use of hybrid methods in condition monitoring is presented. Finally, various considerations for developing hybrid methods for planetary gearbox diagnostics and prognostics are discussed. This includes considerations for selecting an appropriate hybrid framework, pre-processing of the measured vibration signal, physics-based modelling of a planetary gearbox, model calibration, health state estimation, and health state prediction.