The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.

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The current state of the art in electronic prognostic health management systems does not fully support detection, collection, and remediation of real-time faults. As a result, knowledge has not been captured from an actual platform failure mechanism. Thus, point-of-failure feedback cannot be applied by system designers or operators to improve lifecycle weak links in replacement platforms, or to strengthen effectiveness of mission-critical platforms. Our innovation makes it possible to extract and analyze the power system's eigenvalues, which are related to the intrinsic frequencies of the power system that determine correlations between extracted features and state of health (SoH). In-situ electronic prognostics for power systems are crucial for attaining a sound theoretical basis of health status. To provide correlation information such as state of health (SOH) using pattern analysis with real-time data from a non-intrusive smart power sensor, Ridgetop researched using data-driven modeling with a proposed health distance and Support Vector Machines (SVMs) with signatures in a standard IEEE 1451-enabled smart power sensor. Results of this study indicate that a fault pattern analysis methodology overcomes certain disadvantages of the standard failure modes and effects analysis (FMEA) approach, which does not account for the contribution of unobserved failure to a degradation trajectory. The efficacy of the proposed pattern analysis approach is illustrated with test results showing critical distinction in pattern analysis and test data acquired from a real-time IEEE 1451-enabled smart power sensor testbed, and monitored via a testbed with appropriate instrumentation. 1 2