Airborne Laser Diagnosis & Prognosis

We focus on the development of a method for data-based, online, real-time monitoring of machine health state and predicting imminent failures. The data driven prognostic system is based on a new, general, state-space based approach to parameter tracking in dynamical systems. This method is applicable to systems where the parameters drift at a slower rate than the observable dynamics measured by sensors. This method is applied to a gray-scale health monitoring and imminent failure prediction in the Airborne Laser (ABL) subsystems. This is accomplished by developing enabling software technologies that will utilize readily available operating data and sensor measurements to monitor systems in real time so that the incipient damage can be tracked and time to failure can be predicted, complete with error estimates. To assist users in analyzing the variables associated with damages, an unsupervised neural network is used to classify the measurement data and a computer visualization program shows high-dimensional patterns.