Condition prognosis of mechanical drives based on nonlinear dynamical models

Forecasting the condition of the equipment is becoming an important ingredient of the advanced maintenance and asset management systems. In this paper a probabilistic approach to the prognosis of damage progression in gearboxes is presented. It is based on a stochastic nonlinear grey-box model of the underlying wear phenomena. Model parameters are estimated from the available vibration records by using an iterative Maximum Likelihood procedure. The procedure relies on the Expectation-Maximization algorithm and the Unscented Kalman filter for estimation of hidden system states. The algorithm has been used to predict the normal operating horizon of a single-stage gearbox system. Several test runs of the system have been preformed to validate the algorithm.