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This presentation will discuss Ir for online diagnostics with acoustics; Ptot, Ttot, Pstat rake surveys for mean flow measurements; focused Schleiren for density and some turbulence structure; Laser sheet visualization for near-nozzle diagnostics; and Two-point hotwire measurements for turbulence models.
System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.
Laser-velocimeter covariance processor calculates autocovariance and cross-covariance functions for turbulent flow field, based on Poissonsampled measurements in time from laser velocimeter. Hardware implementation of correlation technique to laser velocimeter flow-field diagnostic system fast enough for online production applications.
This Component-Level Electronic-Assembly Repair (CLEAR) Operational Concept document was developed as a first step in developing the Component-Level Electronic-Assembly Repair (CLEAR) System Architecture (NASA/TM-2011-216956). The CLEAR operational concept defines how the system will be used by the Constellation Program and what needs it meets. The document creates scenarios for major elements of the CLEAR architecture. These scenarios are generic enough to apply to near-Earth, Moon, and Mars missions. The CLEAR operational concept involves basic assumptions about the overall program architecture and interactions with the CLEAR system architecture. The assumptions include spacecraft and operational constraints for near-Earth orbit, Moon, and Mars missions. This document addresses an incremental development strategy where capabilities evolve over time, but it is structured to prevent obsolescence. The approach minimizes flight hardware by exploiting Internet-like telecommunications that enables CLEAR capabilities to remain on Earth and to be uplinked as needed. To minimize crew time and operational cost, CLEAR exploits offline development and validation to support online teleoperations. Operational concept scenarios are developed for diagnostics, repair, and functional test operations. Many of the supporting functions defined in these operational scenarios are further defined as technologies in NASA/TM-2011-216956.
This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.
NASA's Ames Research Center awarded Ciencia, Inc., a Small Business Innovation Research contract to develop the Cell Fluorescence Analysis System (CFAS) to address the size, mass, and power constraints of using fluorescence spectroscopy in the International Space Station's Life Science Research Facility. The system will play an important role in studying biological specimen's long-term adaptation to microgravity. Commercial applications for the technology include diverse markets such as food safety, in situ environmental monitoring, online process analysis, genomics and DNA chips, and non-invasive diagnostics. Ciencia has already sold the system to the private sector for biosensor applications.
An innovative approach to fault management was recently demonstrated for the NASA LeRC Space Station Freedom (SSF) power system testbed. This project capitalized on research in model-based reasoning, which uses knowledge of a system's behavior to monitor its health. The fault management system (FMS) can isolate failures online, or in a post analysis mode, and requires no knowledge of failure symptoms to perform its diagnostics. An in-house tool called MARPLE was used to develop and run the FMS. MARPLE's capabilities are similar to those available from commercial expert system shells, although MARPLE is designed to build model-based as opposed to rule-based systems. These capabilities include functions for capturing behavioral knowledge, a reasoning engine that implements a model-based technique known as constraint suspension, and a tool for quickly generating new user interfaces. The prototype produced by applying MARPLE to SSF not only demonstrated that model-based reasoning is a valuable diagnostic approach, but it also suggested several new applications of MARPLE, including an integration and testing aid, and a complement to state estimation.
This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBES's) in the process industries. After a brief overview of the features of a KBES useful in process applications, the general roles of KBES's are covered. A particular focus is diagnostic applications, one of the major applications areas. Many applications are seen as an expansion of supervisory control. The lessons learned from numerous online applications are summarized.
Simulations using reanalyzed meteorological conditions have been long used to understand causes of atmospheric composition change over the recent past. Using the new Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorology, chemistry simulations are being conducted to create products covering 1980-2016 for the atmospheric composition community. These simulations use the Global Modeling Initiative (GMI) chemical mechanism in two different models: the GMI Chemical Transport Model (CTM) and the GEOS-5 model developed Replay mode. Replay mode means an integration of the GEOS-5 general circulation model that is incrementally adjusted each time step toward the MERRA-2 analysis. The GMI CTM is a 1 x 1.25 simulation and the MERRA-2 GMI Replay simulation uses the native MERRA-2 approximately horizontal resolution on the cubed sphere. The Replay simulations is driven by the online use of key MERRA-2 meteorological variables (i.e. U, V, T, and surface pressure) with all other variables calculated in response to those variables. A specialized set of transport diagnostics is included in both runs to better understand trace gas transport and changes over the recent past.
Wound care issues and the ability to establish intravenous (IV) access among injured or ill crew members are a source of concern for NASA flight surgeons. Indeed, the microgravity environment and the remote nature of the International Space Station (ISS) pose unique challenges in diagnosing and treating an injured astronaut. Therefore, it is necessary to identify and adapt the best evidence based terrestrial practices regarding wound care, hemostasis, and IV access for use on the ISS. Methods: A panel of consultants was convened to evaluate the adequacy of the current ISS in-flight medical system for diagnosis and treatment of wounds and establishing IV access by a nonclinician crew medical officer. Participants were acknowledged experts in terrestrial wound care and/or operational medicine. Prior to the meeting, each panelist was encouraged to participate in a pre-summit online forum. Results: Eight external experts participated in a face-to-face meeting held at NASA-Johnson Space Center. Recommendations were made to augment the space station pharmacopoeia, as well as current wound care diagnostic, therapeutic, and deorbit criteria protocols. Additionally, suggestions were offered regarding IV access techniques and devices for use in the microgravity environment. Discussion: The results of the expert panel provide an evidence-based approach to the diagnosis and care of wounds in an injured astronaut on aboard the ISS. The results of the panel underscored the need for further research in wound therapy and IV access devices.
A diagnostic tool was developed for detecting fatigue damage to spur gears, spiral bevel gears, and rolling element bearings. This diagnostic tool was developed and evaluated experimentally by collecting oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig, Spiral Bevel Gear Test Facility, and the 500hp Helicopter Transmission Test Stand. During each test, data from an online, in-line, inductance type oil debris sensor was monitored and recorded for the occurrence of pitting damage. Results indicate oil debris alone cannot discriminate between bearing and gear fatigue damage.
We present here a comprehensive study of the optical/near-infrared (IR) upper limits for gamma-ray bursts that have an X-ray afterglow. We have extrapolated the X-ray afterglows to optical wavelengths based on the physics of the fireball blast wave model and compared these results with optical upper limits for a large sample of bursts. We find a small set of only three bursts out of a sample of 20 for which the upper limits are not compatible with their X-ray afterglow properties within the context of any blast wave model. This sparse sample does not allow us to conclusively determine the cause of this optical/near-IR deficit. Extinction in the host galaxy is a likely cause, but high redshifts and different afterglow mechanisms might also explain the deficit in some cases. We note that the three bursts appear to have higher than average gamma-my peak fluxes. In a magnitude versus time diagram the bursts are separated from the majority of bursts with a detected optical/near-IR afterglow. However, two gamma- ray bursts with an optical afterglow (one of which is highly reddened) also fall in this region with dark bursts, making it likely that dark bursts are at the faint end of the set of optically detected bursts, and therefore the dark bursts likely form a continuum with the bursts with a detected optical afterglow. Our work provides a useful diagnostic tool for follow-up observations for potentially dark bursts; applied to the events detected with the Swift satellite, it will significantly increase our sample of truly dark bursts and shed light upon their nature. Subject headings: dust, extinction - gamma rays: bursts Online material: machine-readable tables
Continuous online monitoring of complex dynamic systems is common in applications as diverse as industrial plant operations, telecommunications network, and biomedical health monitoring. For monitoring purposes, the exact nature of the system under observation is typically not relevant provided there exist some measurements or symptoms which provide diagnostics information regarding the underlying system state.
Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.
The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.
The age of air has recently emerged as a diagnostic of atmospheric transport unaffected by chemical parameterizations, and the features in the age distributions computed in models have been interpreted in terms of the models' large-scale circulation field. This study shows, however, that in addition to the simulated large-scale circulation, three-dimensional age calculations can also be affected by the choice of advection scheme employed in solving the tracer continuity equation, Specifically, using the 3.0deg latitude X 3.6deg longitude and 40 vertical level version of the Geophysical Fluid Dynamics Laboratory SKYHI GCM and six online transport schemes ranging from Eulerian through semi-Lagrangian to fully Lagrangian, it will be demonstrated that the oldest ages are obtained using the nondiffusive centered-difference schemes while the youngest ages are computed with a semi-Lagrangian transport (SLT) scheme. The centered- difference schemes are capable of producing ages older than 10 years in the mesosphere, thus eliminating the "young bias" found in previous age-of-air calculations. At this stage, only limited intuitive explanations can be advanced for this sensitivity of age-of-air calculations to the choice of advection scheme, In particular, age distributions computed online with the National Center for Atmospheric Research Community Climate Model (MACCM3) using different varieties of the SLT scheme are substantially older than the SKYHI SLT distribution. The different varieties, including a noninterpolating-in-the-vertical version (which is essentially centered-difference in the vertical), also produce a narrower range of age distributions than the suite of advection schemes employed in the SKYHI model. While additional MACCM3 experiments with a wider range of schemes would be necessary to provide more definitive insights, the older and less variable MACCM3 age distributions can plausibly be interpreted as being due to the semi-implicit semi-Lagrangian dynamics employed in the MACCM3. This type of dynamical core (employed with a 60-min time step) is likely to reduce SLT's interpolation errors that are compounded by the short-term variability characteristic of the explicit centered-difference dynamics employed in the SKYHI model (time step of 3 min). In the extreme case of a very slowly varying circulation, the choice of advection scheme has no effect on two-dimensional (latitude-height) age-of-air calculations, owing to the smooth nature of the transport circulation in 2D models. These results suggest that nondiffusive schemes may be the preferred choice for multiyear simulations of tracers not overly sensitive to the requirement of monotonicity (this category includes many greenhouse gases). At the same time, age-of-air calculations offer a simple quantitative diagnostic of a scheme's long-term diffusive properties and may help in the evaluation of dynamical cores in multiyear integrations. On the other hand, the sensitivity of the computed ages to the model numerics calls for caution in using age of air as a diagnostic of a GCM's large-scale circulation field.
The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. The CMIS can be used in situ with a minimum amount of user intervention. This system, which was developed at the NASA Glenn Research Center, can scan, find areas of interest, focus, and acquire images automatically. Large numbers of multiple cell experiments require microscopy for in situ observations; this is only feasible with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control capabilities. The software also has a user-friendly interface that can be used independently of the hardware for post-experiment analysis. CMIS has potential commercial uses in the automated online inspection of precision parts, medical imaging, security industry (examination of currency in automated teller machines and fingerprint identification in secure entry locks), environmental industry (automated examination of soil/water samples), biomedical field (automated blood/cell analysis), and microscopy community. CMIS will improve research in several ways: It will expand the capabilities of MSD experiments utilizing microscope technology. It may be used in lunar and Martian experiments (Rover Robot). Because of its reduced size, it will enable experiments that were not feasible previously. It may be incorporated into existing shuttle orbiter and space station experiments, including glove-box-sized experiments as well as ground-based experiments.