Hermetic Reaction Bonded Silicon Carbide Tube Joints with Microwave Heating

Not Available New advances in electric motor technology coupled with dramatic reductions in life-cycle costs and weight have driven aircraft designers to incorporate new electric-powered actuation devices into their near-term designs. The failure of these electromechanical actuator (EMA) components is expected, but difficult to predict. Worst-case EMA failures can have tragic results, so detecting failure before it occurs is highly desirable. By analyzing both electrical modes, plus performance parameters of an operating EMA, one can determine the extent of degradation without adding additional sensors or requiring dismantling the EMA and performing a detailed inspection of its components. This condition-based maintenance will result in large cost savings by extending the useful life of mechanical components, extending normal maintenance cycles, and safeguarding against premature component failure. ORINCON proposes to develop a prognostic algorithm development system for automated detection and prediction of faults in an operating EMA. Our EMA Health Diagnostic (EHD) toolkit system uses advanced spectral estimation techniques, model-based condition algorithms, data fusion, and neural network processing to detect mechanical flaws and predict failures. The proposed system will enable advanced prognostic algorithm development based on operational EMA data using ORINCON's advanced spectral estimation techniques, neural net processing, and multisensor fusion.