Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples

Advances in type-II superlattice research at Fraunhofer IAF

Current advances in type-II superlattice (T2SL) research at Fraunhofer IAF are elaborated on in this paper. First, the use of metastructures for quantum efficiency (QE) enhancement in the longwave infrared (LWIR) is presented. Finite element modelling results are reported on that suggest a potential for doubling of the QE at certain wavelengths with the investigated device structure. Next, characterisation results of midwave infrared (MIWR) InAs/InAsSb T2SL nBn detectors are shown. The low, diffusion-limited dark current above 120 K and a QE of 60% are comparable to the state-of-the-art. Finally, groundwork for InAs/GaSb T2SL MWIR/LWIR dual-band detector arrays based on a back-to-back heterojunction diode device concept is presented. The dry etching technology allows for steep etch trenches and full pixel reticulation with a fill factor of about 70% at 12 μm pitch. The detector characterisation at 77 K and ±250 mV bias demonstrates the bias-switchable operation mode with dark current densities of 6.1·10ˉ⁹ A/cm² in the MWIR and 5.3·10ˉ⁴ A/cm² in the LWIR