Maintenance costs, depending on the specific industry, can represent a large percentage of the costs of the manufactured product. In the aerospace/aeronautic industry, the reduction of maintenance costs, safety and long-life operations in engine and power equipment has been the top priorities. Accurate component life prognostic methodologies are sought to effectively schedule the maintenance downtime as well as to insure accomplishments of long-range missions. This proposal aimed at the development of an innovative machine health and prognosis system for gear transmission and the bearing support systems. Gear damage monitoring will be conducted using a joint time-frequency analysis on rotor vibration signatures. The damage level will be quantified using an optimal tracker on the vibration data. Wear/damage in the bearing will be detected and quantify using bearing housing vibration signals with a chaotic distribution scheme. Analytically, a cohesive constitute model will be established using finite element approach to predict the growth of the cracks in both gear and bearing components. Using the damage parameter developed through the cohesive crack model and the existing damage quantified by the signal processing procedures, the remaining life of the damaged component will be prognosticated by a recently developed nonlinear regression procedure. The fault detection techniques, the damage quantification schemes, and the remaining life prognostication procedures will be integrated into a pre-prototype system to provide an overall health diagnosis and prognosis for gear and bearing components.
