Probabilistic multibody dynamic analysis of gear systems for wind turbines

Nowadays, the reliability of wind turbine gearbox (WTG) is one of the biggest concerns remaining in the wind industry. WTGs have been prone to early failure rather than any mechanical part of modern wind turbines and are subjected to torsional and non-torsional loads which are not yet properly assessed. They are reported to fail within three to seven years, as opposed to the expected twenty years of operation. Their downtime and maintenance process is the most costly of any failure of subassembly of WTs. To improve the reliability of WTGs, a novel Probabilistic MultiBody Dynamic Analysis (PMBDA) framework has been developed and implemented. Gears and Gear systems, like any other mechanical system, have design parameter and loading uncertainties emanating from inherent randomness, measurement and manufacturing errors. However, state-of-the-art models of WTGs and wind turbine drive trains consider deterministic loading and design parameters. This research addresses the early life failure of gearboxes of wind turbines and inquires if a novel approach termed as PMBDA will have an impact on enhancing the reliability of gear systems of wind turbines. An implementation framework that amalgamates the two broad disciplines, probabilistic analysis and multibody dynamic analysis, has been developed, several loading, assembly and design parameters are considered as random variables and their uncertainty is quantified. Using multibody dynamics software called MSC.ADAMS, contact based rigid multibody NASA test-rig gear pairs, a rigid-body and flexible WTG’s high-speed-parallel-helical-stage gear pairs and a flexible WTG’s compound-planetary-helical-stage models, with variable loading, assembly and design parameters, were developed. An advanced fast probability integration methods and standard/advanced sampling techniques were implemented to perform a probabilistic analysis of tooth dynamic force, dynamic factor, root bending stress, tooth surface compressive stress and fatigue life of gears. Probabilistic sensitivities of the performance functions to several random variables were also determined. In addition to revealing system reliability or under-performance through probability of failure (Pf), PMBDA approach also helps designers to consider certain variables critically through probabilistic sensitivity results.