Fault classification with Gauss-Newton optimization and real-time simulation

Kim, Byoung Uk; Lynn, Chris; Kunst, Neil; Vohnout, Sonia; Goebel, Kai

doi:10.1109/aero.2011.5747564

Cited by 4 publications

(1 citation statement)

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“…In previous research, 4,5 the authors designed a MIL-STD-1553 bus monitor and a MIL-STD-1553 bus controller to simulate an aircraft data bus, read the environmental (i.e., altitude) and operational (i.e., response of system) data of a system, and determine whether a fault is manifesting; and if true, determine the root cause and symptoms of the fault. This paper presents the continuation of that research, where the authors leverage Ridgetop's EMA prognostics methodology to further develop the MAPR and describe a ground-based prototype of the technology to show the efficacy of this data analysis and fault classification method.…”

Section: American Institute Of Aeronautics and Astronauticsmentioning

confidence: 99%

A Model-based Avionic Prognostic Reasoner (MAPR)

Vohnout

Kim

Kunst

et al. 2012

Infotech@Aerospace 2012

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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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Section: American Institute Of Aeronautics and Astronauticsmentioning

confidence: 99%