Engineering topics

Brownston, Lee

Publications and source records attributed to Brownston, Lee.

5 records

NASA NTRS2015

From Pixels to Planets

The Kepler Mission was launched in 2009 as NASAs first mission capable of finding Earth-size planets in the habitable zone of Sun-like stars. Its telescope consists of a 1.5-m primary mirror and a 0.95-m aperture. The 42 charge-coupled devices in its focal plane are read out every half hour, compressed, and then downlinked monthly. After four years, the second of four reaction wheels failed, ending the original mission. Back on earth, the Science Operations Center developed the Science Pipeline to analyze about 200,000 target stars in Keplers field of view, looking for evidence of periodic dimming suggesting that one or more planets had crossed the face of its host star. The Pipeline comprises several steps, from pixel-level calibration, through noise and artifact removal, to detection of transit-like signals and the construction of a suite of diagnostic tests to guard against false positives. The Kepler Science Pipeline consists of a pipeline infrastructure written in the Java programming language, which marshals data input to and output from MATLAB applications that are executed as external processes. The pipeline modules, which underwent continuous development and refinement even after data started arriving, employ several analytic techniques, many developed for the Kepler Project. Because of the large number of targets, the large amount of data per target and the complexity of the pipeline algorithms, the processing demands are daunting. Some pipeline modules require days to weeks to process all of their targets, even when run on NASA's 128-node Pleiades supercomputer. The software developers are still seeking ways to increase the throughput. To date, the Kepler project has discovered more than 4000 planetary candidates, of which more than 1000 have been independently confirmed or validated to be exoplanets. Funding for this mission is provided by NASAs Science Mission Directorate.

supercomputers↗

NASA NTRS2012

HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

Narasimhan, Sriram↗

NASA NTRS2007

Automated Diagnosis and Control of Complex Systems

Livingstone2 is a reusable, artificial intelligence (AI) software system designed to assist spacecraft, life support systems, chemical plants, or other complex systems by operating with minimal human supervision, even in the face of hardware failures or unexpected events. The software diagnoses the current state of the spacecraft or other system, and recommends commands or repair actions that will allow the system to continue operation. Livingstone2 is an enhancement of the Livingstone diagnosis system that was flight-tested onboard the Deep Space One spacecraft in 1999. This version tracks multiple diagnostic hypotheses, rather than just a single hypothesis as in the previous version. It is also able to revise diagnostic decisions made in the past when additional observations become available. In such cases, Livingstone might arrive at an incorrect hypothesis. Re-architecting and re-implementing the system in C++ has increased performance. Usability has been improved by creating a set of development tools that is closely integrated with the Livingstone2 engine. In addition to the core diagnosis engine, Livingstone2 includes a compiler that translates diagnostic models written in a Java-like language into Livingstone2's language, and a broad set of graphical tools for model development.

Kurien, James↗

NASA NTRS2004

Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates whose consistency can be tracked across multiple time steps.

Narasimhan, Sriram↗

NASA NTRS2002

The NASA Integrated Vehicle Health Management Technology Experiment for X-37

The NASA Integrated Vehicle Health Management (IVHM) Technology Experiment for X-37 was intended to run IVHM software on-board the X-37 spacecraft. The X-37 is intended to be an unpiloted vehicle that would orbit the Earth for up to 21 days before landing on a runway. The objectives of the experiment were to demonstrate the benefits of in-flight IVHM to the operation of a Reusable Launch Vehicle, to advance the Technology Readiness Level of this IVHM technology within a flight environment, and to demonstrate that the IVHM software could operate on the Vehicle Management Computer. The scope of the experiment was to perform real-time fault detection and isolation for X-37's electrical power system and electro-mechanical actuators. The experiment used Livingstone, a software system that performs diagnosis using a qualitative, model-based reasoning approach that searches system-wide interactions to detect and isolate failures. Two of the challenges we faced were to make this research software more efficient so that it would fit within the limited computational resources that were available to us on the X-37 spacecraft, and to modify it so that it satisfied the X-37's software safety requirements. Although the experiment is currently unfunded, the development effort had value in that it resulted in major improvements in Livingstone's efficiency and safety. This paper reviews some of the details of the modeling and integration efforts, and some of the lessons that were learned.

Schwabacher, Mark↗