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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 253 records · Page 14

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NASA NTRS1993

Pattern classifier for health monitoring of helicopter gearboxes

The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95 percent when the faults were included in training.

Chin, Hsinyung↗

NASA NTRS1998

Breaking the Wavelength Barrier

Under an SBIR (Small Business Innovative Research) contract with Goddard Space Flight Center, Focused Research, Inc. and New Focus, Inc., developed a single mode tunable diode laser from 1.9 to 4.0 microns. As an offshoot from this work, they were able to introduce a 2.0 micron diode laser into their commercial product line. These lasers are useful in trace gas detection, environmental monitoring, seeding of lidar systems, and medical diagnostic experiments.

Source record↗

NASA NTRS2004

High Sensitivity, Low Power Nano Sensors and Devices for Chemical Sensing

The chemical sensor market has been projected to grow to better than $40 billion dollars worldwide within the next 10 years. Some of the primary motivations to develop nanostructured chemical sensors are monitoring and control of environmental pollution; improved diagnostics for consumption; improvement in measurement precision and accuracy; and improved detection limits for Homeland security, battlefield environments, and process and quality control of industrial applications. In each of these applications, there is demand for sensitivity, selectivity and stability of environmental and biohazard detection and capture beyond what is currently commercially available. Nanotechnology offers the ability to work at the molecular level, atom by atom, to create large structures with fundamentally new molecular organization. It is essentially concerned with materials, devices, and systems whose structures and components exhibit novel and significantly improved physical, chemical and biological properties, phenomena, and process control due to their nanoscale size. One such nanotechnology-enabled chemical sensor has been developed at NASA Ames leveraging nanostructures, such as single walled carbon nanotubes (SWNTs) and metal oxide nanobelts or nanowires, as a sensing medium bridging a pair of interdigitated electrodes (IDE) realized through a silicon-based microfabrication and micromachining technique. The DE fingers are fabricated on a silicon substrate using standard photolithography and thin film metallization techniques. It is noteworthy that the fabrication techniques employed are not confined to the silicon substrate. Through spin casting and careful substrate selection (i.e. clothing, glass, polymer, etc.), additional degrees of freedom can be exploited to enhance sensitivity or to conform to unique applications. Both in-situ growth of nanostructured materials and casting of nanostructured dispersions were used to produce analogous chemical sensing devices.

Li, Jing↗

NASA NTRS2006

Inferring Gear Damage from Oil-Debris and Vibration Data

A system for real-time detection of surface-fatigue-pitting damage to gears for use in a helicopter transmission is based on fuzzy-logic used to fuse data from sensors that measure oil-borne debris, referred to as "oil debris" in the article, and vibration signatures. A system to detect helicopter-transmission gear damage is beneficial because the power train of a helicopter is essential for propulsion, lift, and maneuvering, hence, the integrity of the transmission is critical to helicopter safety. To enable detection of an impending transmission failure, an ideal diagnostic system should provide real-time monitoring of the "health" of the transmission, be capable of a high level of reliable detection (with minimization of false alarms), and provide human users with clear information on the health of the system without making it necessary for them to interpret large amounts of sensor data.

Dempsey, Paula↗

NASA NTRS2022

Lithium-ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

Lithium-ion batteries, depth of discharge, strain ↗

NASA NTRS2006

Tapered Roller Bearing Damage Detection Using Decision Fusion Analysis

A diagnostic tool was developed for detecting fatigue damage of tapered roller bearings. Tapered roller bearings are used in helicopter transmissions and have potential for use in high bypass advanced gas turbine aircraft engines. A diagnostic tool was developed and evaluated experimentally by collecting oil debris data from failure progression tests conducted using health monitoring hardware. Failure progression tests were performed with tapered roller bearings under simulated engine load conditions. Tests were performed on one healthy bearing and three pre-damaged bearings. During each test, data from an on-line, in-line, inductance type oil debris sensor and three accelerometers were monitored and recorded for the occurrence of bearing failure. The bearing was removed and inspected periodically for damage progression throughout testing. Using data fusion techniques, two different monitoring technologies, oil debris analysis and vibration, were integrated into a health monitoring system for detecting bearing surface fatigue pitting damage. The data fusion diagnostic tool was evaluated during bearing failure progression tests under simulated engine load conditions. This integrated system showed improved detection of fatigue damage and health assessment of the tapered roller bearings as compared to using individual health monitoring technologies.

Dempsey, Paula J.↗

NASA NTRS1991

Knowledge-based Autonomous Test Engineer (KATE)

Mathematical models of system components have long been used to allow simulators to predict system behavior to various stimuli. Recent efforts to monitor, diagnose, and control real-time systems using component models have experienced similar success. NASA Kennedy is continuing the development of a tool for implementing real-time knowledge-based diagnostic and control systems called KATE (Knowledge based Autonomous Test Engineer). KATE is a model-based reasoning shell designed to provide autonomous control, monitoring, fault detection, and diagnostics for complex engineering systems by applying its reasoning techniques to an exchangeable quantitative model describing the structure and function of the various system components and their systemic behavior.

Parrish, Carrie L.↗

NASA NTRS1981

Performance deterioration based on simulated aerodynamic loads test, JT9D jet engine diagnostics program

An engine was specially prepared with extensive instrumentation to monitor performance, case temperatures, and clearance changes. A special loading device was used to apply known loads on the engine by the use of cables placed around the flight inlet. These loads simulated the estimated aerodynamic pressure distributions that occur on the inlet in various segments of a typical airplane flight. Test results indicate that the engine lost 1.3 percent in take-off thrust specific fuel consumption (TSFC) during the course of the test effort. Permanent clearance changes due to the loads accounted for 1.1 percent; increase in low pressure compressor airfoil roughness and thermal distortion in the high pressure turbine accounted for 0.2 percent. Pretest predicted performance loss due to clearance changes was 0.9 percent in TSFC. Therefore, the agreement between measurement and prediction is considered to be excellent.

Stromberg, W. J.↗

NASA NTRS2014

Smart Sensors Gather Information for Machine Diagnostics

Stennis Space Center was interested in using smart sensors to monitor components on test stands and avert equipment failures. Partnering with St. Paul, Minnesota-based Lion Precision through a Cooperative Agreement, the team developed a smart sensor and the associated communication protocols. The same sensor is now commercially available for manufacturing.

Source record↗

NASA NTRS2005

Traffic-Light-Preemption Vehicle-Transponder Software Module

A prototype wireless data-communication and control system automatically modifies the switching of traffic lights to give priority to emergency vehicles. The system, which was reported in several NASA Tech Briefs articles at earlier stages of development, includes a transponder on each emergency vehicle, a monitoring and control unit (an intersection controller) at each intersection equipped with traffic lights, and a central monitoring subsystem. An essential component of the system is a software module executed by a microcontroller in each transponder. This module integrates and broadcasts data on the position, velocity, acceleration, and emergency status of the vehicle. The position, velocity, and acceleration data are derived partly from the Global Positioning System, partly from deductive reckoning, and partly from a diagnostic computer aboard the vehicle. The software module also monitors similar broadcasts from other vehicles and from intersection controllers, informs the driver of which intersections it controls, and generates visible and audible alerts to inform the driver of any other emergency vehicles that are close enough to create a potential hazard. The execution of the software module can be monitored remotely and the module can be upgraded remotely and, hence, automatically

Bachelder, Aaron↗

NASA NTRS2013

Distributed Prognostics and Health Management with a Wireless Network Architecture

A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.

Goebel, Kai↗

NASA NTRS2010

Improved Signal Processing Technique Leads to More Robust Self Diagnostic Accelerometer System

The self diagnostic accelerometer (SDA) is a sensor system designed to actively monitor the health of an accelerometer. In this case an accelerometer is considered healthy if it can be determined that it is operating correctly and its measurements may be relied upon. The SDA system accomplishes this by actively monitoring the accelerometer for a variety of failure conditions including accelerometer structural damage, an electrical open circuit, and most importantly accelerometer detachment. In recent testing of the SDA system in emulated engine operating conditions it has been found that a more robust signal processing technique was necessary. An improved accelerometer diagnostic technique and test results of the SDA system utilizing this technique are presented here. Furthermore, the real time, autonomous capability of the SDA system to concurrently compensate for effects from real operating conditions such as temperature changes and mechanical noise, while monitoring the condition of the accelerometer health and attachment, will be demonstrated.

Tokars, Roger↗

NASA NTRS1989

Acoustic emission monitoring of SSME-ATD roller bearings

Advanced acoustic emission (AE) monitoring methods are being designed to provide a diagnostic capability for ball and roller bearings as part of a program to develop AE sensors, data acquisition hardware, and analysis techniques that can be used to assess the health of a bearing during the operation of the SSME high pressure fuel turbopump and high pressure oxygen turbopump. Preliminary results are presented from six tests of different roller bearing designs in a rig which simulated the speed, load, and temperature environment encountered in the turbopumps. Correlations were found between the bearing clearances, applied loads, rotation speeds, and roller element stability with the AE signal structure.

Hawman, M. W.↗

NASA NTRS1992

Lab-scale Lidar Sensing of Diesel Engines Exhausts

Combustion technology and its environmental concerns are being considered with increasing attention, not only for global-scale effects, but also for toxicological implications, particularly in the lift conditions of traffic-congested areas and industrial sites. Majority combustion by-products (CO, NO(sub x)) and unburned hydrocarbons (HC), are already subject to increasingly severe regulations; however other, non-regulated minority species, mainly soot and heavy aromatic molecules, involve higher health risks, as they are suspected to be agents of serious pathologies and even mutagenic effects. This is but one of the reasons why much research work is being carried out worldwide on the physical properties of these substances. Correspondingly, the need arises to detect their presence in urban environments, with as high a sensitivity as is required by their low concentrations, proper time- and space-resolutions, and 'real-time' capabilities. Lidar techniques are excellent candidates to this purpose, although severe constraints limit their applicability, eye-safety problems and aerosol Mie scattering uncertainties above all. At CNR's Istituto Motori in Napels, a Lidar-like diagnostic system is being developed, aimed primarily at monitoring the dynamic behavior of internal combustion engines, particularly diesel exhausts, and at exploring the feasibility of a so-called 'Downtown Lidar'.

Borghese, A.↗

NASA NTRS1991

NASA Space Engineering Research Center for utilization of local planetary resources

Because of a change in the NASA funding cycle, the present reporting period covers only the six months from March to September 1991. Nevertheless, remarkable progress was made in a number of areas, some of the most noteworthy of which are: (1) Engineering operation of a breadboard CO2 yields O2 demonstration plant that produced over 10 grams of oxygen per day during several runs of over 100 hours each with a single electrolytic cell. Complete automation of controls, monitoring of various inputs/outputs and critical internal variables, diagnostics, and emergency shutdown in an orderly manner were also included. Moreover, 4-cell and 16-cell units, capable of much higher rates of production, were assembled and tested. (2) Demonstration of a 200 percent increase in the carbothermal reduction of ilmenite through vapor deposition of carbon layers on particles of that material. (3) Demonstration of the deposition of strong iron films from carbonyl chemical vapor deposition, establishing the crucial role of additive gases in governing the process. (4) Discovery of an apparent 800 percent increase in the conversion rates of a modified ilmenite simulant in a plasma-augmented reactor, including direct enhancement by solar radiation absorption. (5) Proof that test specimens of lunar soil with small amounts of metallic additives, recrystallized at moderate temperatures, exhibit an improvement of several orders of magnitude in ductility/tensile strength. (6) Experiments establishing the feasibility of producing silicon-based polymers from indigenous lunar materials. (7) Application of CCD technology to the production of maps of TiO2 abundance, defining primary ilmenite deposits, on the disk of the full moon. (8) Attainment of a discovery rate of approximately 3 new near-Earth asteroids per month by Spacewatch, more than doubling the previous global rate. (9) Coordination of industry and university magma electrolysis investigations in a workshop designed to define remaining problem areas and propose critical experiments.

Ramohalli, Kumar↗

NASA NTRS1994

Quantitative 3-D imaging topogrammetry for telemedicine applications

The technology to reliably transmit high-resolution visual imagery over short to medium distances in real time has led to the serious considerations of the use of telemedicine, telepresence, and telerobotics in the delivery of health care. These concepts may involve, and evolve toward: consultation from remote expert teaching centers; diagnosis; triage; real-time remote advice to the surgeon; and real-time remote surgical instrument manipulation (telerobotics with virtual reality). Further extrapolation leads to teledesign and telereplication of spare surgical parts through quantitative teleimaging of 3-D surfaces tied to CAD/CAM devices and an artificially intelligent archival data base of 'normal' shapes. The ability to generate 'topogrames' or 3-D surface numerical tables of coordinate values capable of creating computer-generated virtual holographic-like displays, machine part replication, and statistical diagnostic shape assessment is critical to the progression of telemedicine. Any virtual reality simulation will remain in 'video-game' realm until realistic dimensional and spatial relational inputs from real measurements in vivo during surgeries are added to an ever-growing statistical data archive. The challenges of managing and interpreting this 3-D data base, which would include radiographic and surface quantitative data, are considerable. As technology drives toward dynamic and continuous 3-D surface measurements, presenting millions of X, Y, Z data points per second of flexing, stretching, moving human organs, the knowledge base and interpretive capabilities of 'brilliant robots' to work as a surgeon's tireless assistants becomes imaginable. The brilliant robot would 'see' what the surgeon sees--and more, for the robot could quantify its 3-D sensing and would 'see' in a wider spectral range than humans, and could zoom its 'eyes' from the macro world to long-distance microscopy. Unerring robot hands could rapidly perform machine-aided suturing with precision micro-sewing machines, splice neural connections with laser welds, micro-bore through constricted vessels, and computer combine ultrasound, microradiography, and 3-D mini-borescopes to quickly assess and trace vascular problems in situ. The spatial relationships between organs, robotic arms, and end-effector diagnostic, manipulative, and surgical instruments would be constantly monitored by the robot 'brain' using inputs from its multiple 3-D quantitative 'eyes' remote sensing, as well as by contact and proximity force measuring devices. Methods to create accurate and quantitative 3-D topograms at continuous video data rates are described.

Altschuler, Bruce R.↗

NASA NTRS2005

Reliability Assessment of Advanced Flip-clip Interconnect Electronic Package Assemblies under Extreme Cold Temperatures (-190 and -120 C)

Flip-chip interconnect electronic package boards have been assembled, underfilled, non-destructively evaluated and subsequently subjected to extreme temperature thermal cycling to assess the reliability of this advanced packaging interconnect technology for future deep space, long-term, extreme temperature missions. In this very preliminary study, the employed temperature range covers military specifications (-55 C to 100 C), extreme cold Martian (-120 C to 115 C) and asteroid Nereus (-180 C to 25 C) environments. The resistance of daisy-chained, flip-chip interconnects were measured at room temperature and at various intervals as a function of extreme temperature thermal cycling. Electrical resistance measurements are reported and the tests to date have not shown significant change in resistance as a function of extreme temperature thermal cycling. However, the change in interconnect resistance becomes more noticeable with increasing number of thermal cycles. Further research work has been carried out to understand the reliability of flip-chip interconnect packages under extreme temperature applications (-190 C to 85 C) via continuously monitoring the daisy chain resistance. Adaptation of suitable diagnostic techniques to identify the failure mechanisms is in progress. This presentation will describe the experimental test results of flip-chip testing under extreme temperatures.

thermal cycling↗

NASA NTRS2019

Verification of the SMAP Level-4 Soil Moisture Analysis Using Rainfall Observations in Australia

Global, 3-hourly, 9-km resolution soil moisture estimates are available with a mean latency of ~2.5 days from the NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product. These estimates are based on the assimilation of SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Routine monitoring of the L4_SM system's assimilation diagnostics revealed occasionally large observation-minus-forecast Tb differences across eastern central Australia that resulted in large analysis increments (or adjustments) of the model forecast soil moisture. Because this region lacks in situ soil moisture measurements, we developed an alternative approach to assess the veracity of the soil moisture analysis increments in the L4_SM system. Using regional gauge-based precipitation data, we demonstrate that the L4_SM soil moisture increments are correlated with errors in the L4_SM precipitation forcing, suggesting that the SMAP Tb observations contribute valuable information to the L4_SM soil moisture estimates.

Reichle, R.↗