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NASA NTRS2001

Principal Components Analysis of Triaxial Vibration Data From Helicopter Transmissions

Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, were explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. In this paper, the PCA transformation is applied to 176 test conditions/data sets collected from an OH58C helicopter to derive the overall experiment-wide covariance matrix and its principal eigenvectors. The experiment-wide eigenvectors. are then projected onto the individual test conditions to evaluate changes and similarities in their directionality based on the various experimental factors. The paper will present the foundations of the proposed approach, addressing the question of whether experiment-wide eigenvectors accurately model the vibration modes in individual test conditions. The results will further determine the value of using directionality and triaxial accelerometers for vibration monitoring and anomaly detection.

Tumer, Irem Y.↗

NASA NTRS2002

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Tumer, Irem Y.↗

NASA NTRS2002

Observation of Nonthermal Emission from the Supernova Remnant IC443 with RXTE

In this paper we present analysis of X-ray spectra from the supernova remnant IC443 obtained using the PCA on RXTE. The spectra in the 3 - 20 keV band are well fit by a two-component model consisting of thermal and nonthermal components. We compare these results with recent results of other X-ray missions and discuss the need for a cut-off in the nonthermal spectrum. Recent Chandra and XMM-Newton observations suggest that much of the nonthermal emission from IC443 can be attributed to a pulsar wind nebula. We present the results of our search for periodic emission in the RXTE PCA data. We then discuss the origin o f the nonthermal component and its possible association with the unidentified EGRET source.

Sturner, S. J.↗

NASA NTRS2000

Dimension Reduction of Hyperspectral Data on Beowulf Clusters

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operation. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold a great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, which is used widely in remote sensing, is the Principal Components Analysis (PCA). In light of the growing number of spectral channels of modern instruments, the paper reports on the development of a parallel PCA and its implementation on two Beowulf cluster configurations, on with fast Ethernet switch and the other is with a Myrinet interconnection.

El-Ghazawi, Tarek↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Lunar Crust as Sampled by Basins and Craters

The session "Lunar Crust as Samples by Basins and Craters" included:Radar Properties of Lunar Basin Deposits; Numerical Modeling of the South Pole-Aitkin Impact; Lunar South Pole-Aitken Impact Basin: Topography and Mineralogy; Comparison of the Geologic Setting of the South Pole-Aitken Basin Interior with Apollo 16: Implications for Regolith Components; Identifying Impact Events Within the Lunar Cataclysm from 40Ar-39Ar Ages of Apollo 16 Impact Melt Rocks; Apollo 16 Mafic Glass: Geochemistry, Provenance, and Implications; Lunar Meteorite PCA 02 007: A Feldspathic Regolith Breccia with Mixed Mare/Highland Components; Compositional Constraints on the Launch Pairing of LAP 02205 and PCA 02007 with Other Lunar Meteorites; An In-Situ Study of REE Abundances in Three Anorthositic Impact Melt Lunar Highland Meteorites; A Crustal Rock Clast in Magnesian Anorthositic Breccia, Dhofar 489 and Its Excavation from a Large Basin; The Origin and Impact History of Lunar Meteorite Yamato 86032; Evolved Lithologies and Their Inferred Sources in the Northwestern Procellarum Region of the Moon; and Revisiting the Interpretation of Thorium Abundances at Hansteen Alpha.

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NASA NTRS2004

Petrography of Lunar Meteorite PCA02007, a New Feldspathic Regolith Breccia

PCA 02007 is a 22.4 g lunar meteorite collected in 2003 near the Pecora Escarpment in Antarctica [1]. PCA is a feldspathic regolith breccia composed of mature regolith. It is compositionally and texturally similar to other feldspathic lunar meteorites (FLMs) [2] and may be launch paired with Yamato 791197 [3]. Here we present a petrographic description and compositions of mineral clasts, glass clasts, lithic clasts, and the bulk meteorite.

Zeigler, R. A.↗

NASA NTRS2000

RXTE and BATSE Observations of the Recent Outbursts of 2S 1417-624

25 1417-624 is a 57 mHz transient Be/X-ray pulsar system first discovered by SAS 3 in 1978. After the discovery outburst it was not seen again until 1994 when BATSE observed an outburst that peaked at 40 Srab (pulsed flux, 20-50 keV) and lasted 110 days, followed by a series of 5 shorter, 10-15 mCrab outbursts occurring every 42 days. Pulse timing analysis showed that these smaller outbursts began a few days after periastron passage of the system's eccentric, 42 day period orbit. Renewed activity from the source was detected by BATSE beginning in 1999 November. BATSE monitoring shows a 10 mCrab peak flux outburst lasting 10 days, followed in the next orbit by a 30 mCrab peak flux outburst, which to date has lasted 56 days. Regular monitoring with RXTE began during the 1999 November outburst and has continued in to the current outburst. The data provides good coverage of the low flux state between BATSE detections, which includes the periastron passage. Pulses are detected through this interval, showing that the source did not enter the propeller regime. The PCA monitoring from the lowest flux level, which occurs shortly before periastron, until the peak of the current outburst, show a continuous evolution of the pulse profile. These profiles contain a narrow notch feature which allow us to present a phase aligned history of this pulse evolution. We present a timing analysis of the PCA and BATSE observations that provide corrections to the binary orbit, and measurements of the torque during the observations. From the observed torque and flux we show that disc accretion is occurring in the current outburst, most probably was also occurring in the smaller outburst.

Finger, M. H.↗

NASA NTRS2004

The Spectrum of the Cosmic X-ray Background Observed by RTXE/PCA

We have analyzed a large set of Rossi X-ray Timing Explorer/Proportional Counter Array (RXTE/PCA) scanning and slewing observations performed between April 1996 and March 1999. We obtained the 3-20 keV spectrum of the cosmic X-ray background (CXB) by subtracting Earth-occulted observations from observations of the X-ray sky at high galactic latitude and far away from sources. The sky coverage is approximately 22.6 x 10(exp 3) square degrees. The PCA spectrum of CXB in 3-20 keV energy band is adequately approximated by a single power law with photon index GAMMA approximately 1.4 and normalization at 1 keV approximately 9.5 phot/s/square centimeter/keV/sr. Instrumental background uncertainty precludes accurate RXTE/PCA measurements of the spectrum of cosmic X-ray background at energies above 15 keV and therefore we cannot detect the high energy cutoff observed by the High Energy Astronomical Observatory (HEAO)-1 A2 experiment. Deep observations of the 6 high latitude points used to model the PCA background provide a coarse measure of the spatial variation of the CXB. The CXB variations are consistent with a fixed spectral shape and variable normalization characterized by a fractional rms amplitude of approximately 7% on angular scales of approximately 1 square deg.

Revnivtsev, M.↗

NASA NTRS2008

Recent Improvements to the Calibration Models for RXTE/PCA

We are updating the calibration of the PCA to correct for slow variations, primarily in energy to channel relationship. We have also improved the physical model in the vicinity of the Xe K-edge, which should increase the reliability of continuum fits above 20 keV. The improvements to the matrix are especially important to simultaneous observations, where the PCA is often used to constrain the continuum while other higher resolution spectrometers are used to study the shape of lines and edges associated with Iron.

Jahoda, K.↗

NASA NTRS2008

Interannual Variability and Trends of Extratropical Ozone: Northern Hemisphere - Part 1

The authors apply principal component analysis (PCA) to the extratropical total column ozone from the combined merged ozone data product and the European Centre for Medium-Range Weather Forecasts assimilated ozone from January 1979 to August 2002. The interannual variability (IAV) of extratropical O-3 in the Northern Hemisphere (NH) is characterized by four main modes. Attributable to dominant dynamical effects, these four modes account for nearly 60% of the total ozone variance in the NH. The patterns of variability are distinctly different from those derived for total O-3 in the tropics. To relate the derived patterns of O-3 to atmospheric dynamics, similar decompositions are performed for the 30 100-Wa geopotential thickness. The results reveal intimate connections between the IAV of total ozone and the atmospheric circulation. The first two leading modes are nearly zonally symmetric and represent the connections to the annular modes and the quasi-biennial oscillation. The other two modes exhibit in-quadrature, wavenumber-1 structures that, when combined, describe the displacement of the polar vortices in response to planetary waves. In the NH, the extrema of these combined modes have preferred locations that suggest fixed topographical and land-sea thermal forcing of the involved planetary waves. Similar spatial patterns and trends in extratropical column ozone are simulated by the Goddard Earth Observation System chemistryclimate model (GEOS-CCM). The decreasing O-3 trend is captured in the first mode. The largest trend occurs at the North Pole, with values similar to-1 Dobson Unit (DU) yr(-1). There is almost no trend in tropical O-3. The trends derived from PCA are confirmed using a completely independent method, empirical mode decomposition, for zonally averaged O-3 data. The O-3 trend is also captured by mode 1 in the GEOS-CCM, but the decrease is substantially larger than that in the real atmosphere.

Yung, Yuk L.↗

NASA NTRS2005

Method of Real-Time Principal-Component Analysis

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Duong, Tuan↗

NASA NTRS2005

Real-Time Principal-Component Analysis

A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

Duong, Vu↗

NASA NTRS2011

Comparison of the Booster Interface Temperature in Stainless Steel (SS) V-Channel versus the Aluminum (Al) Y-Channel Primer Chamber Assemblies (PCAs): Technical Assessment Report - Volume 1

NASA's Technical Fellow for Propulsion, requested a technical assessment of the performance improvement achieved by the introduction of the stainless steel (SS) V-channel compared to the aluminum (Al) Y-channel Primer Chamber Assembly (PCA) design. The SS V-channel PCA was developed for NASA's Mars Science Laboratory (MSL) Project. The principle focus of the assessment was to measure the transient temperature at the booster interface with both designs. This document contains the findings of the assessment.

Garcia, Roberto↗

NASA NTRS2011

Comparison of the Booster Interface Temperature in Stainless Steel (SS) V-Channel Versus the Aluminum (Al) Y-Channel Primer Chamber Assemblies (PCAs): Appendices - Volume 2

Garcia, Roberto↗

NASA NTRS2012

Monitoring the Crab Nebula with LOFT

From 2008-2010, the Crab Nebula was found to decline by 7% in the 15-50 keV band, consistently in Fermi GBM, INTEGRAL IBIS, SPI, and JEMX, RXTE PCA, and Swift BAT. From 2001-2010, the 15-50 keV flux from the Crab Nebula typically varied by about 3.5% per year. Analysis of RXTE PCA data suggests possible spectral variations correlated with the flux variations. I will present estimates of the LOFT sensitivity to these variations. Prior to 2001 and since 2010, the observed flux variations have been much smaller. Monitoring the Crab with the LOFT WFM and LAD will provide precise measurements of flux variations in the Crab Nebula if it undergoes a similarly active episode.

Wilson-Hodge, Colleen A.↗

NASA NTRS2012

Temporal Variability of Observed and Simulated Hyperspectral Earth Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system designed to study Earth's climate variability with unprecedented absolute radiometric accuracy and SI traceability. Observation System Simulation Experiments (OSSEs) were developed using GCM output and MODTRAN to simulate CLARREO reflectance measurements during the 21st century as a design tool for the CLARREO hyperspectral shortwave imager. With OSSE simulations of hyperspectral reflectance, Feldman et al. [2011a,b] found that shortwave reflectance is able to detect changes in climate variables during the 21st century and improve time-to-detection compared to broadband measurements. The OSSE has been a powerful tool in the design of the CLARREO imager and for understanding the effect of climate change on the spectral variability of reflectance, but it is important to evaluate how well the OSSE simulates the Earth's present-day spectral variability. For this evaluation we have used hyperspectral reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), a shortwave spectrometer that was operational between March 2002 and April 2012. To study the spectral variability of SCIAMACHY-measured and OSSE-simulated reflectance, we used principal component analysis (PCA), a spectral decomposition technique that identifies dominant modes of variability in a multivariate data set. Using quantitative comparisons of the OSSE and SCIAMACHY PCs, we have quantified how well the OSSE captures the spectral variability of Earth?s climate system at the beginning of the 21st century relative to SCIAMACHY measurements. These results showed that the OSSE and SCIAMACHY data sets share over 99% of their total variance in 2004. Using the PCs and the temporally distributed reflectance spectra projected onto the PCs (PC scores), we can study the temporal variability of the observed and simulated reflectance spectra. Multivariate time series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.

Roberts, Yolanda↗

NASA NTRS2013

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

Roberts, Yolanda, L.↗

NASA NTRS2013

Principal Components Analysis of a JWST NIRSpec Detector Subsystem

We present principal component analysis (PCA) of a flight-representative James Webb Space Telescope NearInfrared Spectrograph (NIRSpec) Detector Subsystem. Although our results are specific to NIRSpec and its T - 40 K SIDECAR ASICs and 5 m cutoff H2RG detector arrays, the underlying technical approach is more general. We describe how we measured the systems response to small environmental perturbations by modulating a set of bias voltages and temperature. We used this information to compute the systems principal noise components. Together with information from the astronomical scene, we show how the zeroth principal component can be used to calibrate out the effects of small thermal and electrical instabilities to produce cosmetically cleaner images with significantly less correlated noise. Alternatively, if one were designing a new instrument, one could use a similar PCA approach to inform a set of environmental requirements (temperature stability, electrical stability, etc.) that enabled the planned instrument to meet performance requirements

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