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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Distributed Impact Detector System (DIDS) Health Monitoring System Evaluation

Damage due to impacts from micrometeoroids and orbital debris is one of the most significant on-orbit hazards for spacecraft. Impacts to thermal protection systems must be detected and the damage evaluated to determine if repairs are needed to allow safe re-entry. To address this issue for the International Space Station Program, Langley Research Center and Johnson Space Center technologists have been working to develop and implement advanced methods for detecting impacts and resultant leaks. LaRC funded a Small Business Innovative Research contract to Invocon, Inc. to develop special wireless sensor systems that are compact, light weight, and have long battery lifetimes to enable applications to long duration space structures. These sensor systems are known as distributed impact detection systems (DIDS). In an assessment, the NASA Engineering and Safety Center procured two prototype DIDS sensor units to evaluate their capabilities in laboratory testing and field testing in an ISS Node 1 structural test article. This document contains the findings of the assessment.

Prosser, William H.↗
NASA NTRS2004

Health Monitoring System for Car Seat

A health monitoring system for use with a child car seat has sensors mounted in the seat to monitor one or more health conditions of the seat's occupant. A processor monitors the sensor's signals and generates status signals related to the monitored conditions. A transmitter wireless transmits the status signals to a remotely located receiver. A signaling device coupled to the receiver produces at least one sensory (e.g., visual, audible, tactile) output based on the status signals.

Susan Vinz Elrod↗
NASA NTRS2004

Inductive System Health Monitoring

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Iverson, David L.↗
NASA NTRS2010

General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations

Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.

Iverson, David L.↗
NASA NTRS1991

Definition, technology readiness, and development cost of the orbit transfer vehicle engine integrated control and health monitoring system elements

An Integrated Control and Health Monitoring (ICHM) system was conceived for use on a 20 Klb thrust baseline Orbit Transfer Vehicle (OTV) engine. Considered for space used, the ICHM was defined for reusability requirements for an OTV engine service free life of 20 missions, with 100 starts and a total engine operational time of 4 hours. Functions were derived by flowing down requirements from NASA guidelines, previous OTV engine or ICHM documents, and related contracts. The elements of an ICHM were identified and listed, and these elements were described in sufficient detail to allow estimation of their technology readiness levels. These elements were assessed in terms of technology readiness level, and supporting rationale for these assessments presented. The remaining cost for development of a minimal ICHM system to technology readiness level 6 was estimated. The estimates are within an accuracy range of minus/plus 20 percent. The cost estimates cover what is needed to prepare an ICHM system for use on a focussed testbed for an expander cycle engine, excluding support to the actual test firings.

Cannon, I.↗
NASA NTRS1999

Space Shuttle Main Engine: Advanced Health Monitoring System

The main gola of the Space Shuttle Main Engine (SSME) Advanced Health Management system is to improve flight safety. To this end the new SSME has robust new components to improve the operating margen and operability. The features of the current SSME health monitoring system, include automated checkouts, closed loop redundant control system, catastropic failure mitigation, fail operational/ fail-safe algorithms, and post flight data and inspection trend analysis. The features of the advanced health monitoring system include: a real time vibration monitor system, a linear engine model, and an optical plume anomaly detection system. Since vibration is a fundamental measure of SSME turbopump health, it stands to reason that monitoring the vibration, will give some idea of the health of the turbopumps. However, how is it possible to avoid shutdown, when it is not necessary. A sensor algorithm has been developed which has been exposed to over 400 test cases in order to evaluate the logic. The optical plume anomaly detection (OPAD) has been developed to be a sensitive monitor of engine wear, erosion, and breakage.

Singer, Chirs↗
NASA NTRS1990

Health Monitoring System for the SSME-fault detection algorithms

A Health Monitoring System (HMS) Framework for the Space Shuttle Main Engine (SSME) has been developed by United Technologies Corporation (UTC) for the NASA Lewis Research Center. As part of this effort, fault detection algorithms have been developed to detect the SSME faults with sufficient time to shutdown the engine. These algorithms have been designed to provide monitoring coverage during the startup, mainstage and shutdown phases of the SSME operation. The algorithms have the capability to detect multiple SSME faults, and are based on time series, regression and clustering techniques. This paper presents a discussion of candidate algorithms suitable for fault detection followed by a description of the algorithms selected for implementation in the HMS and the results of testing these algorithms with the SSME test stand data.

Tulpule, S.↗
NASA NTRS2014

Tunable Laser Development for In-flight Fiber Optic Based Structural Health Monitoring Systems

The objective of this task is to investigate, develop, and demonstrate a low-cost swept lasing light source for NASA DFRC's fiber optics sensing system (FOSS) to perform structural health monitoring on current and future aerospace vehicles. This is the regular update of the Tunable Laser Development for In-flight Fiber Optic Based Structural Health Monitoring Systems website.

Composite Materials↗
NASA NTRS2002

Design and Analysis of Architectures for Structural Health Monitoring Systems

During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.

Mukkamala, Ravi↗
NASA NTRS1990

Health monitoring system for the SSME - Hardware architecture study

This paper presents a hardware architecture for a health monitoring system (HMS) for the SSME. The architecture study was conducted in conjunction with a NASA sponsored program to develop a framework for SSME HMS for (1) ground test and, potentially (2) flight applications. The requirements for both systems are both stated and analyzed. A multiprocessor distributed VME system is envisioned for the ground-test hardware. By repackaging the boards, the same concept is shown to be usable for the flight system. The paper concludes with an analysis of weight, power, and reliability with respect to variations in functionality.

Kamenetz, Jeffry K.↗
NASA NTRS2005

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Iverson, David L.↗
NASA NTRS1996

Health Monitoring System for Composite Structures

An automated system was developed to monitor the health status of composites. It uses the vibration characteristics of composites to identify a component's damage condition. The vibration responses are characterized by a set of signal features defined in the time, frequency and spatial domains. The identification of these changes in the vibration characteristics corresponding to different health conditions was performed using pattern recognition principles. This allows efficient data reduction and interpretation of vast amounts of information. Test components were manufactured from isogrid panels to evaluate performance of the monitoring system. The components were damaged by impact to simulate different health conditions. Free vibration response was induced by a tap test on the test components. The monitoring system was trained using these free vibration responses to identify three different health conditions. They are undamaged vs. damaged, damage location and damage zone size. High reliability in identifying the correct component health condition was achieved by the monitoring system.

Tang, S. S.↗
NASA NTRS1989

Reusable rocket engine turbopump health monitoring system, part 3

Degradation mechanisms and sensor identification/selection resulted in a list of degradation modes and a list of sensors that are utilized in the diagnosis of these degradation modes. The sensor list is divided into primary and secondary indicators of the corresponding degradation modes. The signal conditioning requirements are discussed, describing the methods of producing the Space Shuttle Main Engine (SSME) post-hot-fire test data to be utilized by the Health Monitoring System. Development of the diagnostic logic and algorithms is also presented. The knowledge engineering approach, as utilized, includes the knowledge acquisition effort, characterization of the expert's problem solving strategy, conceptually defining the form of the applicable knowledge base, and rule base, and identifying an appropriate inferencing mechanism for the problem domain. The resulting logic flow graphs detail the diagnosis/prognosis procedure as followed by the experts. The nature and content of required support data and databases is also presented. The distinction between deep and shallow types of knowledge is identified. Computer coding of the Health Monitoring System is shown to follow the logical inferencing of the logic flow graphs/algorithms.

Perry, John G.↗
NASA NTRS2011

Intelligent Wireless Sensor Networks for System Health Monitoring

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

networks↗
NASA NTRS2015

Wearable Health Monitoring Systems

The shrinking size and weight of electronic circuitry has given rise to a new generation of smart clothing that enables biological data to be measured and transmitted. As the variation in the number and type of deployable devices and sensors increases, technology must allow their seamless integration so they can be electrically powered, operated, and recharged over a digital pathway. Nyx Illuminated Clothing Company has developed a lightweight health monitoring system that integrates medical sensors, electrodes, electrical connections, circuits, and a power supply into a single wearable assembly. The system is comfortable, bendable in three dimensions, durable, waterproof, and washable. The innovation will allow astronaut health monitoring in a variety of real-time scenarios, with data stored in digital memory for later use in a medical database. Potential commercial uses are numerous, as the technology enables medical personnel to noninvasively monitor patient vital signs in a multitude of health care settings and applications.

Bell, John↗
NASA NTRS2003

Development and Flight Testing of an Autonomous Landing Gear Health-Monitoring System

Development and testing of an adaptable vehicle health-monitoring architecture is presented. The architecture is being developed for a fleet of vehicles. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and, a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained expert system. Communication between all levels is done with wireless radio frequency interfaces. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear. The flight tests were performed to validate the following: the wireless radio frequency communication capabilities of the system, the hardware design, command and control; software operation; and, data acquisition, storage and retrieval.

Woodard, Stanley E.↗