Assessment of Damage Progression in Automotive Electronics Assemblies Subjected to Temperature and Vibration

2018 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Instantaneous Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Large Data ◽  
Data Sets ◽  
Automotive Electronics ◽  
Damage Progression ◽  
Mode Decomposition ◽  
Electronic Assemblies ◽  
Strain Signal

Electronics in automotive underhood environments is used for a number of safety critical functions. Reliable continued operation of electronic safety systems without catastrophic failure is important for safe operation of the vehicle. There is need for prognostication methods, which can be integrated, with on-board sensors for assessment of accrued damage and impending failure. In this paper, leadfree electronic assemblies consisting of daisy-chained parts have been subjected to high temperature vibration at 5g and 155°C. Spectrogram has been used to identify the emergence of new low frequency components with damage progression in electronic assemblies. Principal component analysis has been used to reduce the dimensionality of large data-sets and identify patterns without the loss of features that signify damage progression and impending failure. Variance of the principal components of the instantaneous frequency has been shown to exhibit an increasing trend during the initial damage progression, attaining a maximum value and decreasing prior to failure. The unique behavior of the instantaneous frequency over the period of vibration can be used as a health-monitoring feature for identifying the impending failures in automotive electronics. Further, damage progression has been studied using Empirical Mode Decomposition (EMD) technique in order to decompose the signals into Independent Mode Functions (IMF). The IMF’s were investigated based on their kurtosis values and a reconstructed strain signal was formulated with all IMF’s greater than a kurtosis value of three. PCA analysis on the reconstructed strain signal gave better patterns that can be used for prognostication of the life of the components.

scholarly journals Mitigation of Striping Noise in ATMS Calibration Counts by Symmetric Filters

2019 ◽  
Vol 36 (7) ◽  
pp. 1297-1312 ◽  
Author(s):  
Xiaoxu Tian ◽  
Xiaolei Zou
Keyword(s):  
Low Frequency ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Advanced Technology ◽  
Filter Method ◽  
Noise Mitigation ◽  
Optimal Filters ◽  
Mode Decomposition ◽  
Before And After ◽  
Detecting Method

AbstractGlobal observations from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership satellite are affected by striping-patterned noise. An optimal symmetric filter method to mitigate the striping noise in warm counts, cold counts, warm load temperatures, and scene counts instead of antenna temperatures is developed and tested in this study. The optimal filters are developed based on the results free of striping noise obtained with a striping noise detecting method by combining the principal component analysis and the ensemble empirical mode decomposition. The two-point algorithm is then used to calculate antenna temperatures with warm counts, cold counts, warm load temperatures, and scene counts before and after applying the optimal filters. The necessity of applying the striping noise mitigation to the scene counts besides the calibration counts (warm and cold counts) is also shown. This explains why the traditional method to smooth only calibration counts has failed to remove the ATMS striping noise. The optimal filters proposed in this study, which remove the high-frequency striping noise without altering low-frequency weather signals, outperform the conventional boxcar filters adopted in the current operational ATMS calibration system.

PCA and ICA Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

2017 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strain Components ◽  
Frequency Components ◽  
Electronic Assemblies

This paper focusses on health monitoring of electronic assemblies under vibration load of 14 G until failure at an ambient temperature of 55 degree Celsius. Strain measurements of the electronic assemblies were measured using the voltage outputs from the strain gauges which are fixed at different locations on the Printed Circuit Board (PCB). Various analysis was conducted on the strain signals include Time-frequency analysis (TFA), Joint Time-Frequency analysis (JTFA) and Statistical techniques like Principal component analysis (PCA), Independent component analysis (ICA) to monitor the health of the packages during the experiment. Frequency analysis techniques were used to get a detailed understanding of the different frequency components before and after the failure of the electronic assemblies. Different filtering algorithms and frequency quantization techniques gave insight about the change in the frequency components with the time of vibration and the energy content of the strain signals was also studied using the joint time-frequency analysis. It is seen that as the vibration time increases the occurrence of new high-frequency components increases and further the amplitude of the high-frequency components also has increased compared to the before failure condition. Statistical techniques such as PCA and ICA were primarily used to reduce the dimensions of the larger data sets and provide a pattern without losing the different characteristics of the strain signals during the course of vibration of electronic assemblies till failure. This helps to represent the complete behavior of the electronic assemblies and to understand the change in the behavior of the strain components till failure. The principal components which were calculated using PCA discretely separated the before failure and after failure strain components and this behavior were also seen by the independent components which were calculated using the Independent Component Analysis (ICA). To quantify the prognostics and hence the health of the electronic assemblies, different statistical prediction algorithms were applied to the coefficients of principal and independent components calculated from PCA and ICA analysis. The instantaneous frequency of the strain signals was calculated and PCA and ICA analysis on the instantaneous frequency matrix was done. The variance of the principal components of instantaneous frequency showed an increasing trend during the initial hours of vibration and after attaining a maximum value it then has a decreasing trend till before failure. During the failure of components, the variance of the principal component decreased further and attained a lowest value. This behavior of the instantaneous frequency over the period of vibration is used as a health monitoring feature.

scholarly journals Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

1993 ◽  
Vol 13 (1) ◽  
pp. 5-14 ◽  
Author(s):  
K. J. Friston ◽  
C. D. Frith ◽  
P. F. Liddle ◽  
R. S. J. Frackowiak
Keyword(s):  
Principal Component Analysis ◽  
Functional Connectivity ◽  
Verbal Fluency ◽  
Principal Component ◽  
Temporal Correlation ◽  
Large Data ◽  
Component Analysis ◽  
Anterior Cingulate ◽  
Data Sets ◽  
Brain System

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

scholarly journals Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

2020 ◽  
Author(s):  
Christiane Scherer ◽  
James Grover ◽  
Darby Kammeraad ◽  
Gabe Rudy ◽  
Andreas Scherer
Keyword(s):  
Principal Component Analysis ◽  
Population Genetics ◽  
Phylogenetic Analysis ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Whole Genomes

AbstractSince the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year.SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

A Practical Robust and Efficient RBF Metamodel Method for Typical Engineering Problems

2008 ◽  
Author(s):  
Xingjie Fang ◽  
Liping Wang ◽  
Don Beeson ◽  
Gene Wiggs
Keyword(s):  
Principal Component ◽  
Large Data ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
Dimensional Model ◽  
Data Set ◽  
Engineering Problems ◽  
Processing Techniques ◽  
Generalization Accuracy

Radial Basis Function (RBF) metamodels have recently attracted increased interest due to their significant advantages over other types of non-parametric metamodels. However, because of the interpolation nature of the RBF mathematics, the accuracy of the model may dramatically deteriorate if the training data set used contains duplicate information, noise or outliers. Also constructing the metamodel may be time consuming whenever the training data sets are large or a high dimensional model is required. In this paper, we propose a robust and efficient RBF metamodeling approach based on data pre-processing techniques that alleviate the accuracy and efficiency issues commonly encountered when RBF models are used in typical real engineering situations. These techniques include 1) the removal of duplicate training data information, 2) the generation of smaller uniformly distributed subsets of training data from large data sets and 3) the quantification and identification of outliers by principal component analysis (PCA) and Hotelling statistics. Simulation results are used to validate the generalization accuracy and efficiency of the proposed approach.

scholarly journals PCA Tomography and Its Application to Nearby Galactic Nuclei

2009 ◽  
Vol 5 (S267) ◽  
pp. 85-89 ◽  
Author(s):  
J. E. Steiner ◽  
R. B. Menezes ◽  
T. V. Ricci ◽  
A. S. de Oliveira
Keyword(s):  
Galactic Nuclei ◽  
Principal Component ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Cubes ◽  
Efficient Information ◽  
Single Field ◽  
Modern Technologies

AbstractWith the development of modern technologies such as integral field units, it is possible to obtain data cubes in which one produces images with spectral resolution. Extracting information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We briefly describe a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses principal component analysis to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. We applied the method, for illustrative purposes, to the central region of the LINER galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, which was not known before. Furthermore, we show that it is displaced from the center of its stellar bulge.

ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 483-491 ◽  
Author(s):  
BRADLEY LEE BARNHART ◽  
HONDA KAHINDO WA NANDAGE ◽  
WILLIAM EICHINGER
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Sets ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Data Gaps ◽  
Discontinuous Data ◽  
Periodic Components

This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data.

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