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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Results of Operator's Evaluation

The objective was to evaluate the feasibility of a state-of-the-art health and usage monitoring system (HUMS) to provide monitoring of critical mechanical systems on the helicopter, including motors, drive train, engines, and life-limited components. The implementation of HUMS and cost integration with current maintenance procedures was assessed from the operator's viewpoint in order to achieve expected benefits from these systems, such as enhanced safety, reduced maintenance cost, and increased availability. An operational HUMS that was installed and operated under an independent flight trial program was used as a basis for this study. The HUMS equipment and software were commercially available. Based on the results of the feasibility study, the HUMS used in the flight trial program generally demonstrated a high level of reliability in monitoring the rotor system, engines, drive train, and life-limited components. The system acted as a sentinel to warn of impending failures. A worn tail rotor pitch bearing was detected by HUMS, which had the capability for self testing to diagnose system and sensor faults. Examples of potential payback to the operator with HUMS were identified, including reduced insurance cost through enhanced safety, lower operating costs derived from maintenance credits, increased aircraft availability, and improved operating efficiency. The interfacing of HUMS with current operational procedures was assessed to require only minimal revisions to the operator's maintenance manuals. Finally the success in realizing the potential benefits from HUMS technology was found to depend on the operator, helicopter manufacturer, regulator (FAA), and HUMS supplier working together.

Romero, Raylund↗
NASA NTRS2013

Integrated Systems Health Management (ISHM) Toolkit

A framework of software components has been implemented to facilitate the development of ISHM systems according to a methodology based on Reliability Centered Maintenance (RCM). This framework is collectively referred to as the Toolkit and was developed using General Atomics' Health MAP (TM) technology. The toolkit is intended to provide assistance to software developers of mission-critical system health monitoring applications in the specification, implementation, configuration, and deployment of such applications. In addition to software tools designed to facilitate these objectives, the toolkit also provides direction to software developers in accordance with an ISHM specification and development methodology. The development tools are based on an RCM approach for the development of ISHM systems. This approach focuses on defining, detecting, and predicting the likelihood of system functional failures and their undesirable consequences.

Venkatesh, Meera↗
DOE OSTI2023

Power Loss and Safety Risks - Recoverable vs. Non-Recoverable?

This presentation presents an update to the PV fleet analysis on PV system degradation. A median system degradation of -0.7 %/year was found for almost 5000 inverter traces. No significant difference between silicon and CdTe technologies were found. Systems with trackers showed a slightly higher degradation. Silicon technologies such as PERC and heterojunction showed degradation similar to conventional technologies but also showed different degradation mechanisms. To achieve long lifetimes early failure must be minimized through qualification testing. Rapid detection during normal life is important as is the control of bill of materials in modules.

degradation↗
DOE OSTI2022

Status of Eddy Current Developments at INL

Both standard and advanced Light Water Reactor (LWR) fuel systems are composed of fuel pellets enclosed in a metallic cladding. The fuel cladding represents the first containment of the fission products and the first safety barrier. Upon progression of irradiation, the cladding properties deteriorate, mainly due to the pick-up of hydrogen, which causes embrittlement, and the consequence corrosion that reduced the metallic substrate. Those changes in the properties combined with the harsh environment, e.g., fatigue and fretting occurring during operation, might eventually lead to loss of hermeticity. Therefore, it is important to be able to quantify the amount of corrosion taking place and to detect and size structural discontinuity that can promote cladding failure. Eddy current testing is one of the non-destructive methods employed for this purpose. Inspections are carried out using special probes, which are designed and optimized to inspect a tube with well-defined geometrical and physical characteristics, i.e. external diameter, wall thickness, material electric conductivity and magnetic permeability. The inspection goals are mainly twofold: first, the determination of the amount of corrosion by measurement of the external oxide thickness; second, the detection of flaws in the tube wall, ether on the external or the internal surface of the tube. This report summarizes the available capabilities for eddy current testing of LWR fuel claddings, identifies needs and are of developments for testing of ATF claddings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Embedded sensors integrated into critical components for in situ health monitoring of steam turbines

Operational flexibility is desired in today’s coal-fired power plants to balance power grids by compensating for the variable electricity supply from renewable energy sources and distributed gensets. This demanding requirement accelerates materials degradation and makes in-situ health monitoring essential. Life monitoring of components and subsystems is thus seen as essential in assessing material and mechanical behavior to estimate system reliability, move to a conditionbased maintenance strategy and determine time to failure of the units in their actual operating conditions. Vibration monitoring can be exploited for blade tip timing to measure blade vibration amplitude and tip clearance to detect any deterioration taking place in the condition of steam turbine blades. A failure of a rotating blade can lead to severe turbine damage followed by extensive repairs and loss of power production. A blade vibration monitoring system can help early detection of abnormal blade vibration behavior. In conjunction with a health monitoring system, the vibration characteristics can be analyzed to support a pro-active maintenance and inspection schedule. While the feasibility of this inspection technique has been amply demonstrated, there is a need to install induction probes to magnetize the blade for signal output. Siemens, in partnership with Raytheon Technologies Research Corporation (RTRC), proposes a holistic approach to develop embedded sensors to utilize radio frequency for not only coupling to sensors, but as the sensing modality. The goal of this project is to “embed” the novel sensing approach by using either additively manufactured or extruded waveguides on rotating blades for recording, evaluation and monitoring of blade vibrations in Low Pressure turbines, with applications extending to aero engines

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2008

Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation

ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model.

Figueroa, Jorge F.↗
NASA NTRS1992

Contact detection and contact motion for error recovery in the presence of uncertainties

Due to various kinds of uncertainties, a robot motion may fail and result in some unintended contact between the object held by the robot and the environment, which greatly hampers robotics applications on tasks with high-precision requirements, such as assembly tasks. Aiming at automatically recovering a robotic task from such a failure, this paper discusses, in the presence of uncertainties, contact detection based on contact motion for recovery. It presents a framework for on-line recognizing contacts using multiple sensor modalities in the presence of sensing uncertainties and means for ensuring successful compliant motions in the presence of sensing and control uncertainties.

Xiao, Jing↗
NASA NTRS2012

Voluntary Consensus Organization Standards for Nondestructive Evaluation of Thin-Walled Metallic Liners and Composite Overwraps in Composite Overwrapped Pressure Vessels

NASA fracture control requirements outlined in NASA-STD-5009 and NASA-STD-5014 are predicated on the availability and use of sensitive nondestructive evaluation (NDE) methods that can detect and monitor defects, thereby providing data that can be used to predict failure or reduce the risk of failure in fracture critical components. However, in the case of composite materials and components, including composite overwrapped pressure vessels (COPVs), the effect of defects is poorly understood, the NDE methods used to evaluate locate and size defects are typically at lower technical readiness level than analogous NDE methods used for metals, and demonstration studies to verify the probability of detection (POD) are generally lacking or unavailable. These factors together make failure prediction of fracture critical composite materials and components based on size, quantity, or orientation of defects nearly impossible. Also, when inspecting metal liners in as-manufactured COPVs, sensitivity is lost and only the inner surface of the liner is accessible. Also, NDE of COPVs as applied during manufacturing varies significantly from manufacturer to manufacturer and has not yet been standardized. Although requirements exist to perform NDE immediately after manufacturing to establish initial integrity of the parts, procedural detail for NDE of composites is still nonexistent or under development. For example, in practice, only a visual inspection of COPVs is performed during manufacturing and service, leaving in question whether defects of concern, for example, bridging, overwrap winding anomalies, impact damage below visible threshold, out-of-family strain growth, and liner buckling have been adequately detected and monitored. To address these shortcomings, in 2005 the NASA Nondestructive Evaluation Working Group (NNWG) began funding work to develop and adopt standards for nondestructive evaluation of aerospace composites in collaboration with the American Society for Testing and Materials (ASTM) Committee E07 on Nondestructive Testing. Similarly, in 2006 the NASA Engineering and Safety Center (NESC) recommended that nondestructive evaluation methods that can predict composite failure in COPVs should be developed and verified, and integrated into the damage control plan for these vessels

Waller, Jess↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1978

Critical fault patterns determination in fault-tolerant computer systems

The method proposed tries to enumerate all the critical fault-patterns (successive occurrences of failures) without analyzing every single possible fault. The conditions for the system to be operating in a given mode can be expressed in terms of the static states. Thus, one can find all the system states that correspond to a given critical mode of operation. The next step consists in analyzing the fault-detection mechanisms, the diagnosis algorithm and the process of switch control. From them, one can find all the possible system configurations that can result from a failure occurrence. Thus, one can list all the characteristics, with respect to detection, diagnosis, and switch control, that failures must have to constitute critical fault-patterns. Such an enumeration of the critical fault-patterns can be directly used to evaluate the overall system tolerance to failures. Present research is focused on how to efficiently make use of these system-level characteristics to enumerate all the failures that verify these characteristics.

Mccluskey, E. J.↗
NASA NTRS1947

Vibration Survey of Blades in 19XB Axial-Flow Compressor: Dynamic Investigation - 2

Strain-gage measurements were taken under operating conditions from blades of various stages of the 19XB axial-flow compressor in an effort to determine the reason for failures in the seventh and tenth stages. First bending-mode vibrations were detected in the first five stages of the compressor caused by each integral multiple of rotor speed from three through ten. Lead-wire failures in the last five stages resulted in incomplete data. The dynamic-vibration frequencies at various rotor speeds were compared with statically measured frequencies analytically corrected for the influence of centrifugal force. Large increases in vibration ani~litude with increased pressure ratio were observed. During surging operation, blade vibrations were not present. The effects of pressure ratio and surge indicate the existence of aerodynamic excitation as the cause of the blade vibrations.

Meyer, Andre J., Jr.↗
NASA NTRS2006

Implantable Wireless MEMS Sensors for Medical Uses

Sensors designed and fabricated according to the principles of microelectromechanical systems (MEMS) are being developed for several medical applications in outer space and on Earth. The designs of these sensors are based on a core design family of pressure sensors, small enough to fit into the eye of a needle, that are fabricated by a "dissolved wafer" process. The sensors are expected to be implantable, batteryless, and wireless. They would be both powered and interrogated by hand-held radio transceivers from distances up to about 6 in. (about 15 cm). One type of sensor would be used to measure blood pressure, particularly for congestive heart failure. Another type would be used to monitor fluids in patients who have hydrocephalus (high brain pressure). Still other types would be used to detect errors in delivery of drugs and to help patients having congestive heart failure.

Chimbayo, Alexander↗
DOE OSTI2021

A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li-Ion and Li-Metal Batteries

Whether attempting to eliminate parasitic Li metal plating on graphite (and other Li-ion anodes) or enabling stable, uniform Li metal formation in ‘anode-free’ Li battery configurations, the detection and characterization (morphology, microstructure, chemistry) of Li that cannot be reversibly cycled is essential to understand the behavior and degradation of rechargeable batteries. In this review, various approaches used to detect and characterize the formation of Li in batteries are discussed. Each technique has its unique set of advantages and limitations, and works towards solving only part of the full puzzle of battery degradation. Going forward, multimodal characterization holds the most promise towards addressing two pressing concerns in the implementation of the next generation of batteries in the transportation sector (viz. reducing recharging times and increasing the available capacity per recharge without sacrificing cycle life). Such characterizations involve combining several techniques (experimental- and/or modeling-based) in order to exploit their respective advantages and allow a more comprehensive view of cell degradation and the role of Li metal formation in it. Additionally, it is also discussed which individual techniques, or combinations thereof, can be implemented in real-world battery management systems on-board electric vehicles for early detection of potential battery degradation that would lead to failure.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This presentation describes the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
DOE OSTI2022

Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor

Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.

42 ENGINEERING↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
NASA NTRS1991

Nondestructive Structural Damage Detection in Flexible Space Structures Using Vibration Characterization

Spacecraft are susceptible to structural damage over their operating life from impact, environmental loads, and fatigue. Structural damage that is not detected and not corrected may potentially cause more damage and eventually catastrophic structural failure. NASA's current fleet of reusable spacecraft, namely the Space Shuttle, has been flown on several missions. In addition, configurations of future NASA space structures, e.g. Space Station Freedom, are larger and more complex than current structures, making them more susceptible to damage as well as being more difficult to inspect. Consequently, a reliable structural damage detection capability is essential to maintain the flight safety of these structures. Visual inspections alone can not locate impending material failure (fatigue cracks, yielding); it can only observe post-failure situations. An alternative approach is to develop an inspection and monitoring system based on vibration characterization that assesses the integrity of structural and mechanical components. A methodology for detecting structural damage is presented. This methodology is based on utilizing modal test data in conjunction with a correlated analytical model of the structure to: (1) identify the structural dynamic characteristics (resonant frequencies and mode shapes) from measurements of ambient motions and/or force excitation; (2) calculate modal residual force vectors to identify the location of structural damage; and (3) conduct a weighted sensitivity analysis in order to assess the extent of mass and stiffness variations, where structural damage is characterized by stiffness reductions. The approach is unique from other existing approaches in that varying system mass and stiffness, mass center locations, the perturbation of both the natural frequencies and mode shapes, and statistical confidence factors for structural parameters and experimental instrumentation are all accounted for directly.

Ricles, James M.↗