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Model Adaptation for Prognostics in a Particle Filtering Framework
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
PSP FIELDS Digital Fields Board (DFB) DC-coupled Differential Voltage, V3-V4 Antennae, Dipole Mode, Bandpass filtered, High Gain, Sensor coordinates, Level 2 (L2), 0.873813 s Data
PSP FIELDS Digital Fields Board, DFB, dV34 data:The DFB is the low frequency, less than 75 kHz, component of the FIELDS experiment on the Parker Solar Probe spacecraft, see reference [1] below. For a full description of the FIELDS experiment, see reference [2]. For a description of the DFB, see reference [3].DFB DC bandpass data consist of peak and average values of the absolute value of bandpassed time series waveform data over a time interval equal to the reporting cadence. The DC bandpass data have the peak response frequency of each bin reported in the metadata. The frequency response curves for these bins are given in [3].The Level 2 data products contained in this data file have been calibrated for:* 1) The ~6.3 dB loss associated with forming the bandpass signal, see reference [3]* 2) DFB in-band gain* 3) The search coil preamplifier response, when applicable* 4) DFB digital filter time delays, the DFB filter time delays become significant in the lowest frequency DC bandpass binsCalibrations for the DFB digital filter gains and analog filter gains have not been implemented as it was determined that these could not be applied accurately to single numerical values representing a broadband signal response and because all bins except the highest frequency bin have a flat gain response equal to one due to these filters. Calibrations for the FIELDS preamplifiers have not been implemented as the preamplifier response is flat and equal to one through the DFB frequency range. Corrections for plasma sheath impedance gain and antenna effective length have not been applied to voltage sensor signals. These corrections will be applied in the Level 3 DFB data products. Therefore, all voltage sensor quantities when present in these Level 2 data products are expressed by using units of Volts. Likewise, all magnetic field quantities when present in these Level 2 data products are expressed by using units of nanoTelsas.The Level 2 data products contained in this data file are expressed in sensor coordinates: e.g. dV12, dV34 for voltage measurements and [u,v,w] for the searchcoil magnetometer.The time resolution of the DFB DC bandpass filtered data can vary by multiples of 2^N. During encounter when PSP is within 0.25 AU of the Sun, the DFB DC bandpass filtered data cadence is typically 1 NYsecond [2]. Timestamps correspond to the center time of each window.References:* 1) Fox, N.J., Velli, M.C., Bale, S.D. et al., Space Sci Rev (2016) 204:7. https://doi.org/10.1007/s1121401502116* 2) Bale, S.D., Goetz, K., Harvey, P.R. et al., Space Sci Rev (2016) 204:49. https://doi.org/10.1007/s1121401602445* 3) Malaspina, D.M., Ergun, R.E., Bolton, M. et al., JGR Space Physics (2016), 121, 5088-5096. https://doi.org/10.1002/2016JA022344
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics
Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α − λ precision, β accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
ROSAT Complete Results Archive Sources for the PSPC with Filter
This table is derived from the Second ROSAT Source Catalog of Pointed Observations with the ROSAT PSPC (Roentgen Satellite Position-Sensitive Proportional Counter) Observed Using the Boron Filter, or the 2RXF Catalog. 2XRF contains arcsecond positions and count rates for 2,526 detected sources from 258 ROSAT PSPC Filter observations covering 0.15% of the sky, including 704 high-confidence detections and 20 obvious sources which were not detected by SASS. This table contains the complete version of the list of detections (2,526 entries), whereas the short 'high-confidence' version (the HEASARC's <a href="/W3Browse/rosat/rospspcf.html">ROSPSPCF table</a>) contains 704 detections. The ROSPSPCFTOT table includes many questionable sources that meet the following parameter criteria: false_det = 'f' or deferred = 'D' or not_checked = 'n'. See the documentation below for descriptions of these parameters. The catalog consists of all primary source parameters from the automated detection algorithm employed by the SASS. In addition each observation has been quality checked, both by automatic algorithms and by detailed visual inspection. The results of this quality checking are contained as a set of logical-value flags for a set of principal source parameters. If a source parameter is suspect, the associated flag is set to a corresponding alphabetical value; parameters with no obvious problems maintain the default, '.', value. The Second ROSAT Pointed PSPC Filter Source Catalog includes missing sources, i.e. obvious sources which were not detected by the SASS source detection software but which could be easily detected by visual inspection. Missed sources are marked by negative values of their source identification number, i.e. the parameter 'MPLSX_ID' has a negative value for these sources. The only tabulated quantities for these visually identified missed sources are source positions; other quantities (like count rates, hardness ratios, etc.) are not available. These data have been screened by ROSAT data centers in the US, Germany, and the UK as a step in the production of the ROSAT Results Archive. The RRA contains extracted source and associated products with an indication of reliability for the primary parameters. More information about the ROSAT mission and the SASS can be obtained from the ROSAT User Handbook, available at <pre> <a href="http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html">http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html</a> </pre> This table was created by the HEASARC in May 2012 based on the file rospspcfcat.txt obtained from the MPE ROSAT Results Archive site (which is no longer available, unfortunately). This is a service provided by NASA HEASARC .
PSP FIELDS Digital Fields Board (DFB) DC-coupled Searchcoil Magnetometer, SCM, Bandpass filtered, Low Frequency, High Gain, u-component, Sensor coordinates, Level 2 (L2), 0.873813 s Data
PSP FIELDS Digital Fields Board, DFB, SCMulfhg data:The DFB is the low frequency, less than 75 kHz, component of the FIELDS experiment on the Parker Solar Probe spacecraft, see reference [1] below. For a full description of the FIELDS experiment, see reference [2]. For a description of the DFB, see reference [3].DFB DC bandpass data consist of peak and average values of the absolute value of bandpassed time series waveform data over a time interval equal to the reporting cadence. The DC bandpass data have the peak response frequency of each bin reported in the metadata. The frequency response curves for these bins are given in [3].The Level 2 data products contained in this data file have been calibrated for:* 1) The ~6.3 dB loss associated with forming the bandpass signal, see reference [3]* 2) DFB in-band gain* 3) The search coil preamplifier response, when applicable* 4) DFB digital filter time delays, the DFB filter time delays become significant in the lowest frequency DC bandpass binsCalibrations for the DFB digital filter gains and analog filter gains have not been implemented as it was determined that these could not be applied accurately to single numerical values representing a broadband signal response and because all bins except the highest frequency bin have a flat gain response equal to one due to these filters. Calibrations for the FIELDS preamplifiers have not been implemented as the preamplifier response is flat and equal to one through the DFB frequency range. Corrections for plasma sheath impedance gain and antenna effective length have not been applied to voltage sensor signals. These corrections will be applied in the Level 3 DFB data products. Therefore, all voltage sensor quantities when present in these Level 2 data products are expressed by using units of Volts. Likewise, all magnetic field quantities when present in these Level 2 data products are expressed by using units of nanoTelsas.The Level 2 data products contained in this data file are expressed in sensor coordinates: e.g. dV12, dV34 for voltage measurements and [u,v,w] for the searchcoil magnetometer.The time resolution of the DFB DC bandpass filtered data can vary by multiples of 2^N. During encounter when PSP is within 0.25 AU of the Sun, the DFB DC bandpass filtered data cadence is typically 1 NYsecond [2]. Timestamps correspond to the center time of each window.References:* 1) Fox, N.J., Velli, M.C., Bale, S.D. et al., Space Sci Rev (2016) 204:7. https://doi.org/10.1007/s1121401502116* 2) Bale, S.D., Goetz, K., Harvey, P.R. et al., Space Sci Rev (2016) 204:49. https://doi.org/10.1007/s1121401602445* 3) Malaspina, D.M., Ergun, R.E., Bolton, M. et al., JGR Space Physics (2016), 121, 5088-5096. https://doi.org/10.1002/2016JA022344
Removing Spikes While Preserving Data and Noise using Wavelet Filter Banks
Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.
PSP FIELDS Digital Fields Board (DFB) DC-coupled Differential Voltage, V1-V2 Antennae, Dipole Mode, Bandpass filtered, High Gain, Sensor coordinates, Level 2 (L2), 0.873813 s Data
PSP FIELDS Digital Fields Board, DFB, dV12 data:The DFB is the low frequency, less than 75 kHz, component of the FIELDS experiment on the Parker Solar Probe spacecraft, see reference [1] below. For a full description of the FIELDS experiment, see reference [2]. For a description of the DFB, see reference [3].DFB DC bandpass data consist of peak and average values of the absolute value of bandpassed time series waveform data over a time interval equal to the reporting cadence. The DC bandpass data have the peak response frequency of each bin reported in the metadata. The frequency response curves for these bins are given in [3].The Level 2 data products contained in this data file have been calibrated for:* 1) The ~6.3 dB loss associated with forming the bandpass signal, see reference [3]* 2) DFB in-band gain* 3) The search coil preamplifier response, when applicable* 4) DFB digital filter time delays, the DFB filter time delays become significant in the lowest frequency DC bandpass binsCalibrations for the DFB digital filter gains and analog filter gains have not been implemented as it was determined that these could not be applied accurately to single numerical values representing a broadband signal response and because all bins except the highest frequency bin have a flat gain response equal to one due to these filters. Calibrations for the FIELDS preamplifiers have not been implemented as the preamplifier response is flat and equal to one through the DFB frequency range. Corrections for plasma sheath impedance gain and antenna effective length have not been applied to voltage sensor signals. These corrections will be applied in the Level 3 DFB data products. Therefore, all voltage sensor quantities when present in these Level 2 data products are expressed by using units of Volts. Likewise, all magnetic field quantities when present in these Level 2 data products are expressed by using units of nanoTelsas.The Level 2 data products contained in this data file are expressed in sensor coordinates: e.g. dV12, dV34 for voltage measurements and [u,v,w] for the searchcoil magnetometer.The time resolution of the DFB DC bandpass filtered data can vary by multiples of 2^N. During encounter when PSP is within 0.25 AU of the Sun, the DFB DC bandpass filtered data cadence is typically 1 NYsecond [2]. Timestamps correspond to the center time of each window.References:* 1) Fox, N.J., Velli, M.C., Bale, S.D. et al., Space Sci Rev (2016) 204:7. https://doi.org/10.1007/s1121401502116* 2) Bale, S.D., Goetz, K., Harvey, P.R. et al., Space Sci Rev (2016) 204:49. https://doi.org/10.1007/s1121401602445* 3) Malaspina, D.M., Ergun, R.E., Bolton, M. et al., JGR Space Physics (2016), 121, 5088-5096. https://doi.org/10.1002/2016JA022344
ROSAT Results Archive Sources for the PSPC with Filter
This table is derived from the Second ROSAT Source Catalog of Pointed Observations with the ROSAT PSPC (Roentgen Satellite Position-Sensitive Proportional Counter) Observed Using the Boron Filter, or the 2RXF Catalog. 2XRF contains arcsecond positions and count rates for 2,526 detected sources from 258 ROSAT PSPC Filter observations covering 0.15% of the sky, including 704 high-confidence detections and 20 obvious sources which were not detected by SASS. The complete version of the list of detections (the HEASARC's <a href="/W3Browse/rosat/rospspcftot.html">ROSPSPCFTOT</a>) table contains 2,526 entries, whereas the short 'high-confidence' version contained in this present table has 704 detection. The ROSPSPCF table excludes sources that meet the following parameter criteria: false_det = 'f' or deferred = 'D' or not_checked = 'n'. See the documentation below for descriptions of these parameters. The catalog consists of all primary source parameters from the automated detection algorithm employed by the SASS. In addition each observation has been quality checked, both by automatic algorithms and by detailed visual inspection. The results of this quality checking are contained as a set of logical-value flags for a set of principal source parameters. If a source parameter is suspect, the associated flag is set to a corresponding alphabetical value; parameters with no obvious problems maintain the default, '.', value. The Second ROSAT Pointed PSPC Filter Source Catalog includes missing sources, i.e. obvious sources which were not detected by the SASS source detection software but which could be easily detected by visual inspection. Missed sources are marked by negative values of their source identification number, i.e. the parameter 'MPLSX_ID' has a negative value for these sources. The only tabulated quantities for these visually identified missed sources are source positions; other quantities (like count rates, hardness ratios, etc.) are not available. These data have been screened by ROSAT data centers in the US, Germany, and the UK as a step in the production of the ROSAT Results Archive. The RRA contains extracted source and associated products with an indication of reliability for the primary parameters. More information about the ROSAT mission and the SASS can be obtained from the ROSAT User Handbook, available at <pre> <a href="http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html">http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html</a> </pre> This table was created by the HEASARC in May 2012 based on the file rospspcfcat-short.txt obtained from the MPE ROSAT Results Archive site (which is no longer available, unfortunately). This is a service provided by NASA HEASARC .
A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components
This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework and specifically for a Kalman filter appli- cation to electronics components. In particular, it explores the implication of modeling remaining useful life prediction as a stochastic process and how it relates to remaining useful life computation by statistical models, to uncertainty representation and management, and to the role of prognostics in decision-making. A discussion on how uncertainty propagates from the health state estimation process through the health state forecasting process is provided. Remaining useful life computation steps under uncertainty are pre- sented and analytical results on uncertainty quantification are provided under a simplified scenario. A proper propagation of uncertainty through the RUL prediction step as well as its correct interpretation are key to developing decision-making methodologies that make use of the remaining useful life prediction estimates and their corresponding uncertainties in order to make actionable choices that will optimize reliability, operations or safety in view of the prognostics information.
IHW COMET HALLEY INFRARED FILTER MEASUREMENTS, V2.0
This data set contains filter curves for all IR filters referenced in any of the data sets archived by the International Halley Watch (IHW) Infrared Studies Network (IRSN).
IHW COMET HALLEY INFRARED FILTER TABLES V1.0
NASA's International Halley Watch (IHW) has created a Comet Halley Archive. The collection of data spans the full wavelength range as submitted by scientists to the IHW. The observations belong to one of the following Disciplines: Amateur, Astrometry, Infrared Studies, Large-Scale Phenomena, Meteor Studies, Near-Nucleus Studies, Photometry and Polarimetry, Radio Studies, and Spectroscopy and Spectrophotometry. The data collected by these nine disciplines were augmented by Spacecraft measurements. The data were submitted to IHW, but the evaluation and selection for the Archive has been the primary responsibility of the Discipline Specialist Teams for each network in cooperation with the Lead Center. The data from the Infrared Studies Network includes transmission tables for 22 filters.
IHW COMET HALLEY INFRARED FILTER CURVE MEASUREMENTS V1.0
NASA's International Halley Watch (IHW) has created a Comet Halley Archive. The collection of data spans the full wavelength range as submitted by scientists to the IHW. The observations belong to one of the following Disciplines: Amateur, Astrometry, Infrared Studies, Large-Scale Phenomena, Meteor Studies, Near-Nucleus Studies, Photometry and Polarimetry, Radio Studies, and Spectroscopy and Spectrophotometry. The data collected by these nine disciplines were augmented by Spacecraft measurements. The data were submitted to IHW, but the evaluation and selection for the Archive has been the primary responsibility of the Discipline Specialist Teams for each network in cooperation with the Lead Center. The data from the Infrared Studies Network includes measurements of the transmission for 46 filter curves.
PSP FIELDS Digital Fields Board (DFB) DC-coupled Searchcoil Magnetometer, SCM, Bandpass filtered, Low Frequency, High Gain, v-component, Sensor coordinates, Level 2 (L2), 0.873813 s Data
PSP FIELDS Digital Fields Board, DFB, SCMvlfhg data:The DFB is the low frequency, less than 75 kHz, component of the FIELDS experiment on the Parker Solar Probe spacecraft, see reference [1] below. For a full description of the FIELDS experiment, see reference [2]. For a description of the DFB, see reference [3].DFB DC bandpass data consist of peak and average values of the absolute value of bandpassed time series waveform data over a time interval equal to the reporting cadence. The DC bandpass data have the peak response frequency of each bin reported in the metadata. The frequency response curves for these bins are given in [3].The Level 2 data products contained in this data file have been calibrated for:* 1) The ~6.3 dB loss associated with forming the bandpass signal, see reference [3]* 2) DFB in-band gain* 3) The search coil preamplifier response, when applicable* 4) DFB digital filter time delays, the DFB filter time delays become significant in the lowest frequency DC bandpass binsCalibrations for the DFB digital filter gains and analog filter gains have not been implemented as it was determined that these could not be applied accurately to single numerical values representing a broadband signal response and because all bins except the highest frequency bin have a flat gain response equal to one due to these filters. Calibrations for the FIELDS preamplifiers have not been implemented as the preamplifier response is flat and equal to one through the DFB frequency range. Corrections for plasma sheath impedance gain and antenna effective length have not been applied to voltage sensor signals. These corrections will be applied in the Level 3 DFB data products. Therefore, all voltage sensor quantities when present in these Level 2 data products are expressed by using units of Volts. Likewise, all magnetic field quantities when present in these Level 2 data products are expressed by using units of nanoTelsas.The Level 2 data products contained in this data file are expressed in sensor coordinates: e.g. dV12, dV34 for voltage measurements and [u,v,w] for the searchcoil magnetometer.The time resolution of the DFB DC bandpass filtered data can vary by multiples of 2^N. During encounter when PSP is within 0.25 AU of the Sun, the DFB DC bandpass filtered data cadence is typically 1 NYsecond [2]. Timestamps correspond to the center time of each window.References:* 1) Fox, N.J., Velli, M.C., Bale, S.D. et al., Space Sci Rev (2016) 204:7. https://doi.org/10.1007/s1121401502116* 2) Bale, S.D., Goetz, K., Harvey, P.R. et al., Space Sci Rev (2016) 204:49. https://doi.org/10.1007/s1121401602445* 3) Malaspina, D.M., Ergun, R.E., Bolton, M. et al., JGR Space Physics (2016), 121, 5088-5096. https://doi.org/10.1002/2016JA022344
Heme_k2_Filtered bckg correct trans-norm
GEO Series GSE8744. Lactiplantibacillus plantarum; Lactiplantibacillus plantarum WCFS1. 4 samples. Type: Expression profiling by array.
Wt-ko_trxB1_Filtered bkg correc transf-norm
GEO Series GSE8672. Lactiplantibacillus plantarum; Lactiplantibacillus plantarum WCFS1. 6 samples. Type: Expression profiling by array.
AER-ANA_Filtered bkgr.correction transf-norm
GEO Series GSE8743. Lactiplantibacillus plantarum; Lactiplantibacillus plantarum WCFS1. 6 samples. Type: Expression profiling by array.
Batch wt vs hyper +_- pABA Normal int_Filtered fg-bg2
GEO Series GSE8956. Lactiplantibacillus plantarum; Lactiplantibacillus plantarum WCFS1. 21 samples. Type: Expression profiling by array.
A Dual-Filter Strategy Integrating CRISPR-based Target Screening and Text Mining for Hand-Foot Syndrome
GEO Series GSE297714. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Control vs hyper; 25 mM glucose_Filtered fb-bg
GEO Series GSE9392. Lactiplantibacillus plantarum WCFS1; Lactiplantibacillus plantarum. 12 samples. Type: Expression profiling by array.
Gene expression in a 3-dimensional human epidermal tissue culture model after water-filtered near-infrared irradiation
GEO Series GSE79394. Homo sapiens. 6 samples. Type: Expression profiling by array.
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