The Airborne Laser Concepts Testbed (ABL ACT) is located on White Sands Missile Range (WSMR), NM and is used to explore and develop new methods for tracking, pointing, and compensation of laser beams. All of these efforts require a knowledge ofthe optical turbulence along the propagation path. The site utilizes a 52.6 km propagation path over a desert basin between two mountain peaks (North Oscuro Peak (NOP) and Salinas Peak (SP)). Characterization of the optical turbulence at ABL ACT is challenging due to the long path length in the atmospheric boundary layer and the complex terrain of the site. A suite of instrumentation is being used to approach the problem; a sodar, fme wire probes, a pupil plane imager, a differential image motion monitor (DIMM), and a scintillometer. In addition, a weather station senses ambient temperature, humidity, pressure, wind speed and direction, and solar radiation-received both horizontally and parallel to the mountain west-facing slope at NOP. Salient features of each instrument as well as the parameters sensed, including pathweighted effects, are discussed. Comparisons of results obtained from different sensors are shown and discussed such as derived from the scintillometer, and pupil plane imager. Special emphasis is given to the optical turbulence conditions at the mountain ridge at NOP which were quantified from observations of fme wire sensors and a sodar (sonic detection and ranging). The results are explained in terms ofthe geometry ofthe site and the mountain-valley wind regime.
Health monitoring systems have evolved into complex diagnostic systems. Researchers are attempting to include prediction, or prognostics, into such systems and are resorting to hybrid systems fusing statistics, data mining, expert systems, neural networks, and more into system that perform not only health monitoring and diagnostics but prognostics as well. However, no work has been reported on systems based on chemical or other non-electrical processes where the interactions of the various operating parameters are subtle, complex, and correlated in unknown or difficult to elicit ways. This paper describes the use of neural networks to provide early detection of the onset of operational failure in such devices and suggests ways to use it as part of a prognostic system.
Successful prognostic and health Muscrave, and Lin [2] describe the use of an automonitoring systems depend on being able to recognize the associative neural network (AANN) in a sensor validation signs of a failure in progress. Although such systems are application for reusable rocket engines. These findings are commonplace, little has been reported to date on fault amplified in a master's thesis by Najafi [3]. Furthermore, detection for systems where the interactions of the various these papers postulate the use of the output of the network operating parameters are subtle, complex, and correlated to replace data lost by failed sensors, due to the AANN's in unknown or difficult to elicit ways. This paper describes property of "correcting" anomalous data. Wang [4] the results of recent research into the use of neural elucidates a neural network gated expert in conjunction networks to provide detection of the onset of operational with the use of an AANN; she also corroborates the use of failure in such devices. After a preliminary exploration the AANN in monitoring the sensors in a health revealed the shortcomings of more common pattern monitoring system. Expert knowledge is used in the recognition methods, such as limit checking, a posteriori PROMISE diagnostic and prognostic system, as reported Baysean methods, and even principal component analysis, by Biagetta and Sciubba [5]. A cogeneration plant in Italy it is shown that certain types of neural networks are up to uses an expert system and system model to augment the the task. The results from simulations will show the health monitoring system. Here, the selection of the effectiveness neural network techniques in detecting the appropriate diagnosis when a detected fault could be onset of the failure. These techniques will then be ascribed to more than one cause, and to provide the demonstrated on data from a real-world system and the prognostic function is aided by expert knowledge and the results presented. system model; once a consensus is achieved, the system then produces a prognostic forecast and suggests measures
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.