Development of data-driven, physics-based, and hybrid prognosis frameworks: a case study for gear remaining useful life prediction

Data-driven, physics-based, and hybrid prognosis frameworks can be developed to estimate remaining useful life, depending on the availability of condition monitoring sensor data and physics-governing equations. No systematic study is available that shows the comparative performance of these frameworks. The present study, for the first time, attempts to show how these three frameworks can be developed under different scenarios and assumptions. The data-driven prognosis framework is developed using an accelerometer signal and an Artificial Intelligence-based random forest regression (RFR) model. A pit growth model inspired by the Paris crack growth law has been used for physics-based prognosis framework development. In this framework, sensor data is needed to know the gear's current health status, as the prognosis framework can't be developed purely on physics. A hybrid prognosis framework is developed using two alternate approaches: one in which current health status is obtained directly from a visual inspection camera and the other in which this status is indirectly inferred from the accelerometer sensor data. In each case, the RUL prediction is made using a physics-based pit growth model coupled with the current health status obtained from either of the two approaches mentioned. To enhance the prediction accuracy, Bayesian inference is used to update the physics-based pit growth model parameters in both hybrid frameworks. Data obtained from five run-to-failure experiments performed on a specially designed gearbox test setup are used to show the comparative performance of these frameworks. The strengths and weaknesses of each of the frameworks are discussed based on the type of data requirement, model definition, parameter estimation, and prediction error.