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At least 253 records · Page 14

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Diagnosis↗

Accuracy study of the Upper Atmosphere Research Satellite (UARS) definitive attitude determination

The Upper Atmosphere Research Satellite (UARS) has two definitive attitude determination requirements: the definitive attitude of the Modular Attitude Control Subsystem (MACS) and the definitive attitude of the gimbaled Solar-Stellar Pointing Platform (SSPP). The onboard computer (OBC) will compute the MACS attitude using a Kalman filter and will transform this attitude solution through the SSPP gimbals to calculate the SSPP attitude. The attitude ground support system (AGSS) will compute the MACS attitude using a batch least-squares differential corrector algorithm and will also transform this solution through the gimbals to obtain the SSPP attitude. This paper reports the results of a prelaunch study to predict the accuracy of the OBC attitude solutions and the accuracy of the AGSS attitude solutions. The OBC and AGSS solution accuracies are then compared to establish the relative quality. The effects of star observability, sensor noise, and sensor misalignment uncertainties on attitude determination accuracy are analyzed for each case.

Snow, Frank↗

COBE ground segment attitude determination

The Cosmic Background Explorer (COBE) spacecraft was launched in November 1989 by NASA to survey the sky for primordial radiation left from the Big Bang explosion. The success of the mission requires an accurate determination of the spacecraft attitude. While the accuracy of the attitude obtained from the attitude sensors is adequate for two of the experiments, the higher accuracy required by the Diffuse Infrared Background Experiment (DIRBE) is obtained by using the DIRBE instrument as a special type of star sensor. Presented here is an overview of the attitude processing algorithms used at the Cosmology Data Analysis Center (CDAC) and the results obtained from the flight data.

Kumar, V. K.↗

Tolerating failures of continuous-valued sensors

One aspect of fault tolerance in process control programs is the ability to tolerate sensor failure. A methodology for transforming a process control program that cannot tolerate sensor failures onto one that can is presented. Issues addressed include modifying specifications in order to accommodate uncertainty in sensor values and averaging sensor values in a fault tolerant manner. In addition, a hierarchy of sensor failure models is identified, and both the attainable accuracy and the run-time complexity of sensor averaging with respect to this hierarchy is discussed.

Marzullo, Keith↗

TRMM On-Orbit Performance Re-Accessed After Control Change

The Tropical Rainfall Measuring Mission (TRMM) spacecraft, a joint mission between the U.S. and Japan, launched onboard an HI1 rocket on November 27,1997 and transitioned in August, 2001 from an average operating altitude of 350 kilometers to 402.5 kilometers. Due to problems using the Earth Sensor Assembly (ESA) at the higher altitude, TRMM switched to a backup attitude control mode. Prior to the orbit boost TRMM controlled pitch and roll to the local vertical using ESA measurements while using gyro data to propagate yaw attitude between yaw updates from the Sun sensors. After the orbit boost, a Kalman filter used 3-axis gyro data with Sun sensor and magnetometers to estimate onboard attitude. While originally intended to meet a degraded attitude accuracy of 0.7 degrees, the new control mode met the original 0.2 degree attitude accuracy requirement after improving onboard ephemeris prediction and adjusting the magnetometer calibration onboard. Independent roll attitude checks using a science instrument, the Precipitation Radar (PR) which was built in Japan, provided a novel insight into the pointing performance. The PR data helped identify the pointing errors after the orbit boost, track the performance improvements, and show subtle effects from ephemeris errors and gyro bias errors. It also helped identify average bias trends throughout the mission. Roll errors tracked by the PR from sample orbits pre-boost and post-boost are shown in Figure 1. Prior to the orbit boost the largest attitude errors were due to occasional interference in the ESA. These errors were sometime larger than 0.2 degrees in pitch and roll, but usually less, as estimated from a comprehensive review of the attitude excursions using gyro data. Sudden jumps in the onboard roll show up as spikes in the reported attitude since the control responds within tens of seconds to null the pointing error. The PR estimated roll tracks well with an estimate of the roll history propagated using gyro data. After the orbit boost, the attitude errors shown by the PR roll have a smooth sine-wave type signal because of the way that attitude errors propagate with the use of gyro data. Yaw errors couple at orbit period to roll with '/4 orbit lag. By tracking the amplitude, phase, and bias of the sinusoidal PR roll error signal, it was shown that the average pitch rotation axis tends to be offset from orbit normal in a direction perpendicular to the Sun direction, as shown in Figure 2 for a 200 day period following the orbit boost. This is a result of the higher accuracy and stability of the Sun sensor measurements relative to the magnetometer measurements used in the Kalman filter. In November, 2001 a magnetometer calibration adjustment was uploaded which improved the pointing performance, keeping the roll and yaw amplitudes within about 0.1 degrees. After the boost, onboard ephemeris errors had a direct effect on the pitch pointing, being used to compute the Earth pointing reference frame. Improvements after the orbit boost have kept the the onboard ephemeris errors generally below 20 kilometers. Ephemeris errors have secondary effects on roll and yaw, especially during high beta angle when pitch effects can couple into roll and yaw. This is illustrated in figure 3. The onboard roll bias trends as measured by PR data show correlations with the Kalman filter's gyro bias error. This particularly shows up after yaw turns (every 2 to 4 weeks) as shown in Figure 3, when a slight roll bias is observed while the onboard computed gyro biases settle to new values. As for longer term trends, the PR data shows that the roll bias was influenced by Earth horizon radiance effects prior to the boost, changing values at yaw turns, and indicated a long term drift as shown in Figure 4. After the boost, the bias variations were smaller and showed some possible correlation with solar beta angle, probably due to sun sensor misalignment effects.

Bilanow, Steve↗

TRMM On-Orbit Performance Reassessed After Control Change

The Tropical Rainfall Measuring Mission (TRMM) spacecraft, a joint mission between the U.S. and Japan, launched onboard an H-I1 rocket on November 27, 1997, and transitioned in August, 2001, from an average operating altitude of 350 kilometers to 402.5 kilometers. Due to problems using the Earth Sensor Assembly (ESA) at the higher altitude, TRMM switched to a backup attitude control mode. Prior to the orbit boost TRMM controlled pitch and roll to the local vertical using ESA measurements while using gyro data to propagate yaw attitude between yaw updates from the Sun sensors. After the orbit boost, a Kalman filter used 3-axis gyro data with Sun sensor and magnetometers to estimate onboard attitude. While originally intended to meet a degraded attitude accuracy of 0.7 degrees, the new control mode met the original 0.2 degree attitude accuracy requirement after improving onboard ephemeris prediction and adjusting the magnetometer calibration onboard. Independent roll attitude checks using a science instrument, the Precipitation Radar (PR) which was built in Japan, provided a novel insight into the pointing performance. The PR data helped identify the pointing errors after the orbit boost, track the performance improvements, and show subtle effects from ephemeris errors and gyro bias errors. It also helped identify average bias trends throughout the mission. Roll errors tracked by the PR from sample orbits pre-boost and post-boost are shown in Figure 1. Prior to the orbit boost, the largest attitude errors were due to occasional interference in the ESA. These errors were sometime larger than 0.2 degrees in pitch and roll, but usually less, as estimated from a comprehensive review of the attitude excursions using gyro data. Sudden jumps in the onboard roll show up as spikes in the reported attitude since the control responds within tens of seconds to null the pointing error. The PR estimated roll tracks well with an estimate of the roll history propagated using gyro data. After the orbit boost, the attitude errors shown by the PR roll have a smooth sine-wave type signal because of the way that attitude errors propagate with the use of gyro data. Yaw errors couple at orbit period to roll with 1/4 orbit lag. By tracking the amplitude, phase, and bias of the sinusoidal PR roll error signal, it was shown that the average pitch rotation axis tends to be offset from orbit normal in a direction perpendicular to the Sun direction, as shown in Figure 2 for a 200 day period following the orbit boost. This is a result of the higher accuracy and stability of the Sun sensor measurements relative to the magnetometer measurements used in the Kalman filter. In November, 2001 a magnetometer calibration adjustment was uploaded which improved the pointing performance, keeping the roll and yaw amplitudes within about 0.1 degrees.

Bilanow, Stephen↗

In-flight comparisons of boundary-layer and wake measurement probes for incompressible flow

The results are presented of in-flight comparisons of a number of boundary-layer and wake measurement probes suitable for low-speed flight-test investigations. The tested boundary-layer probes included a traversing total-pressure probe and a hot-film probe mounted on an internally-mounted drive mechanism, a curved and a straight boundary-layer rake, and a traversing hot-film probe with an externally-mounted drive mechanism. The wake measuring devices included a traversing, self-aligning probe, a wake rake, and an integrating wake rate. The boundary-layer data are compared with a common reference velocity profile and comments given regarding the accuracy of the static-pressure and total-pressure measurements. Discussions on the various calibration presentations used with hot-wire and hot-film sensors and various aspects of improving the accuracy of hot-film sensor results are given in the appendix of this report.

Mertaugh, L. J., Jr.↗

Smart and Intelligent Sensors

John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make a health assessment to inform the data acquisition client when sensor performance is suspect. 3.Distributed sample synchronization. Networks of sensors require new ways for synchronizing samples. Standards that address the distributed timing problem (for example, IEEE STD 1588) provide the means to aggregate samples from many distributed smart sensors with sub-microsecond accuracy. 4. Reduction in interconnect. Alternative means are needed to reduce the frequent problems associated with cabling and connectors. Wireless technologies offer the promise of reducing interconnects and simultaneously making it easy to quickly add a sensor to a system.

Lansaw, John↗

Sensor for distinguishing liquid-vapor phases of superfluid helium

A small, high-accuracy, rapid-response, low-power-dissipation sensor used in determining the distribution of the liquid and vapor phases of helium in a rocket-borne cryogenic cooler is described. The device, which consists of a 6 mm constantan wire coated for about two-thirds of its length with a superconductor alloy, operates on the basis of the coating's alternation between superconductivity (during immersion in the fluid) and a normal resistive state (during immersion in the vapor). The sensor's response time upon shift from one environment to the other is found to be less than 10 milliseconds. Details of design criteria, and installation of an array of sensors in a flight dewar are also given.

Petrac, D.↗

Optical phase measuring sensors for automated rendezvous and capture

A technique is described for sensing relative spatial orientations of approach and target vehicles, using optical phase mensuration (in the interferometric sense, as opposed to LIDAR), in place of the more conventional intensity, image, or transit time measurements. This approach permits the parameters to be measured with great accuracy with relatively simple, small sensors having no moving components. A suite of sensors operating on this principle can produce all desired data using either active detection on the target or passive retroreflection to the detectors on the approach vehicle. These optical phase measurements can be applied to determine bearing angle (location of the target vehicle in the approach vehicle coordinates), range, and attitude (orientation of the target vehicle with respect to the line-of-sight). The first two quantities require the approach vehicle to project a modulated interference pattern into space. The bearing angle is determined for a selected point on the target by measuring the phase of the interference pattern at that point using either a detector on the target or a retroreflector on the target and a detector at the transmitter. The range is found by measuring differential bearing angles to predetermined relative instrumentation sites. Two interferometers, a coarse and a fine ranger are required to resolve the 2pi ambiguity.

Metheny, Wayne↗

SAM-2 ground-truth plan: Correlative measurements for the Stratospheric Aerosol Measurement-2 (SAM 2) sensor on the Nimbus G satellite

The SAM-2 will fly aboard the Nimbus-G satellite for launch in the fall of 1978 and measure stratospheric vertical profiles of aerosol extinction in high latitude bands. The plan gives details of the location and times for the simultaneous satellite/correlative measurements for the nominal launch time, the rationale and choice of the correlative sensors, their characteristics and expected accuracies, and the conversion of their data to extinction profiles. The SAM-2 expected instrument performance and data inversion results are presented. Various atmospheric models representative of polar stratospheric aerosols are used in the SAM-2 and correlative sensor analyses.

Russell, P. B.↗

Computational modeling for the study of multispectral sensor systems and concepts

A computational model of the deterministic and stochastic processes involved in remote sensing is being developed as a tool for studying multispectral sensor systems and concepts. The goal is to improve the efficiency of sensor systems for routine worldwide monitoring of earth resources and the environment. Preliminary computational results are presented for simple models of the natural variability of atmospheric radiative transfer and surface reflectance. These results illustrate the dependence of classification accuracy on the selection of sensor spectral channels and data processing algorithms.

Huck, F. O.↗

The measurement of atomic oxygen in the mesosphere and lower thermosphere

A discussion of the laboratory calibration, calibration simulation, and accuracy of a resonance fluorescence instrument for measuring oxygen atoms is presented. The discussion demonstrates that a sensor can be calibrated with good accuracy to obtain in situ measurements of the oxygen density in the mesosphere/lower thermosphere. Oxygen data are reported from six rocket flights. These data represent four midlatitude flights and two auroral flights. There are two night and two day profiles for the midlatitude experiments. The maximum densities found from these sets of data in the 90-100 km regions are between 1.5 x 10 to the 11th/cu cm and 3.5 x 10 to the 11th/cu cm. Agreement with the MSIS-86 model predictions is obtained for altitudes above 120 km for midlatitude flights. The auroral data are less than the model prediction by about a factor of 2. The data show structure that is probably indicative of gravity wave effects of a nominal 5-km vertical wavelength.

Sharp, William E.↗

SAGE ground truth plan: Correlative measurements for the Stratospheric Aerosol and Gas Experiment (SAGE) on the AEM-B satellite

The ground truth plan is outlined for correlative measurements to validate the Stratospheric Aerosol and Gas Experiment (SAGE) sensor data. SAGE will fly aboard the Applications Explorer Mission-B satellite scheduled for launch in early 1979 and measure stratospheric vertical profiles of aerosol, ozone, nitrogen dioxide, and molecular extinction between 79 N and 79 S. latitude. The plan gives details of the location and times for the simultaneous satellite/correlative measurements for the nominal launch time, the rationale and choice of the correlative sensors, their characteristics and expected accuracies, and the conversion of their data to extinction profiles. In addition, an overview of the SAGE expected instrument performance and data inversion results are presented. Various atmospheric models representative of stratospheric aerosols and ozone are used in the SAGE and correlative sensor analyses.

Russell, P. B.↗

Fault-tolerant system considerations for a redundant strapdown inertial measurement unit

The development and evaluation of a fault-tolerant system for the Redundant Strapdown Inertial Measurement Unit (RSDIMU) being developed and evaluated by the NASA Langley Research Center was continued. The RSDIMU consists of four two-degree-of-freedom gyros and accelerometers mounted on the faces of a semi-octahedron which can be separated into two halves for damage protection. Compensated and uncompensated fault-tolerant system failure decision algorithms were compared. An algorithm to compensate for sensor noise effects in the fault-tolerant system thresholds was evaluated via simulation. The effects of sensor location and magnitude of the vehicle structural modes on system performance were assessed. A threshold generation algorithm, which incorporates noise compensation and filtered parity equation residuals for structural mode compensation, was evaluated. The effects of the fault-tolerant system on navigational accuracy were also considered. A sensor error parametric study was performed in an attempt to improve the soft failure detection capability without obtaining false alarms. Also examined was an FDI system strategy based on the pairwise comparison of sensor measurements. This strategy has the specific advantage of, in many instances, successfully detecting and isolating up to two simultaneously occurring failures.

Motyka, P.↗

Embedded Relative Navigation Sensor Fusion Algorithms for Autonomous Rendezvous and Docking Missions

bd Systems (a subsidiary of SAIC) has developed a suite of embedded relative navigation sensor fusion algorithms to enable NASA autonomous rendezvous and docking (AR&D) missions. Translational and rotational Extended Kalman Filters (EKFs) were developed for integrating measurements based on the vehicles' orbital mechanics and high-fidelity sensor error models and provide a solution with increased accuracy and robustness relative to any single relative navigation sensor. The filters were tested tinough stand-alone covariance analysis, closed-loop testing with a high-fidelity multi-body orbital simulation, and hardware-in-the-loop (HWIL) testing in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL).

DeKock, Brandon K.↗

Turbofan engine demonstration of sensor failure detection

In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at both medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed.

Merrill, Walter C.↗

Estimating Uncertainty in GPCP and TRMM Multi-Satellite Precipitation Estimates

One of the high-priority problems in satellite precipitation estimation is developing algorithms for estimating the errors in precipitation retrievals by individual sensors and subsequent multi-satellite combinations. Classically, we distinguish between "random" and "bias" errors, which do and do not, respectively average to zero over a "big enough" time/space sample. The current operational GPCP and TRMM multi-satellite algorithms are nearly unique in estimating random error for the monthly precipitation estimates from individual sensor systems (including gauge), following Huffman, and then making multi-sensor combinations. No routinely operational global precipitation produces estimates of bias error. Subsequently, a similar scheme has been followed to provide random error estimates for the Multi-satellite Precipitation Analysis (MPA) being computed in real time and after real time for TRMM. The Huffman algorithm for random error is briefly reviewed, including a discussion of the limitations imposed on the algorithm by standard monthly precipitation data sets. Starting from a very simple theoretical treatment of the histogram of precipitation samples in a month, an equation is developed that depends on the estimated average precipitation rate for the month, the number of samples in the month, and two constants. The constants are set separately for each source of precipitation estimate (such as "raingauge") by calibration at selected ground sites. We discuss recent work validating the random error estimates to highlight the successes and limitations of this first-generation approach. We then consider what information is needed from the individual sensor algorithms to facilitate additional accuracy in the estimation of random errors across the time/space span of climate regimes which a global estimation system must handle. In addition, the thorny issue of estimating bias is raised. Finally, the role of error estimates (and the qualitative errors!) in creating combinations of precipitation estimates from different individual sensors is discussed. This issue is particularly important when fine scales in space and time are being considered, say the 0.25 x 0.25-deg 3-hourly estimates in the MPA.

Huffman, G. J.↗