Diagnostics and health monitoring of a dc-dc forward converter through time series analysis

Electronic systems present new challenges in the area of health management. The means by which electronic systems degrade and fail separate themselves from mechanical systems and the approaches used for health management of these systems. This work presents a novel approach to health management and diagnostics for switched-mode dc-dc power supplies. The approach implements Symbolic Analysis in order to statistically model the underlying system dynamics. A healthy baseline model serves as a means to compare future models in order to determine and quantify degradation within the system.;The methodology is validated through four independent accelerated life tests of dc-dc forward converters. It is well known that dc-dc converters do not operate at a single load point and constantly change as needed. The statistical approach uses time series data obtained from the dc-dc converter that is affected by the current loading conditions of the converter. In order to normalize the results, a means to take the loading of the converter into account is also derived and demonstrated. In addition, a complete study in the area of time series data sampling is undertaken for the approach. The work is concluded by demonstrating the approach of prognostication or prediction of remaining useful life using the symbolic methodology by implementation of a linear Kalman Predictor.