![]() They estimate the life of a specific component under specific usage and degradation conditions. Ex: Proportional Hazards Model Type III: Condition-based Individual component based data-driven model These methods also consider the measured or inferred component degradation. They estimate the life of an average component under specific usage conditions. Ex: Weibull Analysis Type II: Stress-based Use population based fault growth model learned from accumulated knowledge These methods also consider the environmental stresses (temperature, load, vibration, etc.) on the component. They estimate the life of a typical component under nominal usage conditions. ![]() The filter capacitor of the power supply is the component which fails most often faulty operation generates navigations errors in INAV Assumption of new functionality increases number of electronics faults with perhaps unanticipated fault modes We need understanding of behavior of deteriorated components to develop capability to anticipate failures/predict remaining RULĥ Motivation (2/2) Line Replaceable Unit: Power Controller Component: Power Transistor Images courtesy : Boeing Components under study: Power MOSFET: IRF520Npbf, TO-220 package, 100V/9.27A IGBT: IRG4BC30KD, TO-220 package, 600V/16A Electrolytic Capacitor: 2200 uf, 10VĦ Definitions So what is Prognostics anyway? prog nos tic M-W.com Something that foretells PHM Community Estimation of the Remaining Useful Life of a component Remaining Useful Life (RUL) The amount of time a component can be expected to continue operating within its stated specifications given: Its current health status, and anticipated future operating conditions Input commands Environment Loadsħ Prognostic Algorithm Categories Type I: Reliability Data-based Use population based statistical model These methods consider historical time to failure data which are used to model the failure distribution. Prognostics & Diagnostics Group Discovery and Systems Health Area (DaSH) Intelligent Systems Division NASA Ames Research Center, California USA Presented at 2 nd European Conference of the PHM Society Nantes, France JP R O G N O S T ICS C E N T E R OF E X C E L L E N C EĢ Agenda Introduction to Prognostics Introduction to Model-based Prognostics Research Approach for Prognostics of Electronics Accelerated Aging as a Prognostics Research Tool Case Study I: Prognostics of Electrolytic Capacitors Model-based approach example Case Study II: Prognostics of Power Transistors Precursors of Failure example Case Study III: Physics-based Prognostics of Capacitors Degradation modeling example Closing Remarksģ Electronics PHM INTRODUCTION TO PROGNOSTICSĤ Motivation (1/2) Future aircraft systems will rely more on electronic components Electronic components have increasingly critical role in on-board, autonomous functions for Vehicle controls, communications, navigation, radar systems Power electronic devices such as power MOSFETs and IGBTs are frequently used in high-power switching circuits The integrated navigation (INAV) module combines output of the GPS model and inertial measurement unit. ![]() Celaya Galván Research Scientist, SGT Inc. 1 Electronics Prognostics Tutorial 4 Presented by Dr.
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