SBIR-STTR Award

ALPHATECH proposes, in conjunction with our partners, Rolls-Royce North America, a novel approach to the prognostic modeling of drivetrain clutch systems, combining dynamic modeling of transients with physics-of-failure based hazard modeling. In particular, our effort is focused towards the F35B LiftFan clutch, to which our models and techniques will be applied. The goal is to predict clutch problems before they impact vehicle performance, enabling the vision of autonomic logistics through precision sensing and prognosis. Our approach entails two components: a modeling part and an estimation part. The model extends existing LiftFan clutch dynamic models to address the transient engagement dynamics, and incorporates several physics-of-failure models for different types of failures. The estimation component uses a Markov approach to compute the current and future fault probabilities, and incorporates a Bayesian data fusion scheme to combine the model-based estimates with anomaly detection estimates. The Markov approach dynamically adapts the hazard used in the life prediction using actual measurements. Further, our physical modeling approach addresses the unique features of this clutch, which has extremely high power density and transient loads. The combined modeling and estimation solution has the potential to provide high-accuracy predictions of remaining useful life. Advanced prognostic models are urgently needed for the LiftFan clutch, which provides an immediate commercial avenue for this research. More generally, drivetrain clutches of the disk type are found on a vast diversity of power transmission systems, from helicopters to cars. An improved prognostic capability in the area of clutch life would be marketable in such areas

ALPHATECH proposes, in conjunction with our partners, Rolls-Royce North America, a novel approach to the on-line prognostic modeling of drivetrain clutch systems, combining dynamic modeling of the clutch transients with physics-of-failure based hazard modeling. In particular, our effort is focused towards the F35B LiftFan clutch, to which our models and techniques will be applied. We extend our oxidation wear model for Carbon-Carbon composites developed under Phase I, and refine this to the LiftFan Clutch application. The goal is to predict clutch problems before they impact vehicle performance, enabling the vision of autonomic logistics through precision sensing and prognosis. The model extends existing LiftFan clutch loading models to address the transient engagement dynamics, and incorporates several physics-of-failure models to address different types of component failures. Further, our physical modeling approach addresses the unique features of this clutch, which has extremely high power density and transient loads. This, combined with the ability to fuse existing anomaly-detection technology, has the potential to provide high-accuracy predictions of future life.

Keywords:
HEALTH MANAGEMENT, FATIGUE MODELING, DRIVETRAIN CLUTCH, DATA FUSION, VIBRATIONAL ANALYSIS, WEAR MODELING, PROGNOSTICS, DIAGNOSTICS