Statistical Pattern Recognition and Built-in Reliability Test for Feature Extraction and Health Monitoring of Electronics under Shock Loads

2007 ◽  
Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameep Gupte ◽  
Jeff Suhling ◽  
James Hofmeister
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Reliability Test ◽  
Shock Loads ◽  
Statistical Pattern

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

Transient-Response Spectral Analysis Based Feature Extraction for Built-In Reliability Test of Electronics Under Shock Loads

2007 ◽  
Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameep Gupte ◽  
Prashant Gupta ◽  
Jeff Suhling ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Health Monitoring ◽  
Solder Ball ◽  
Statistical Pattern Recognition ◽  
Reliability Test ◽  
Leading Indicators ◽  
Time Frequency ◽  
Transient Strain ◽  
Statistical Pattern ◽  
Electronic Assemblies

The built-in stress test (BIST) is extensively used for diagnostics or identification of failure. The current version of BIST approach is focused on reactive failure detection and provides limited insight into reliability and residual life. A new approach has been developed to monitor product-level damage during shock and vibration. The approach focuses on the pre-failure space and methodologies for quantification of failure in electronic equipment subjected to shock and vibration loads using the dynamic response of the electronic equipment. Presented methodologies are applicable at the system-level for identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in shock and drop of electronic assemblies. Three methodologies have been investigated for feature extraction and health monitoring including development of a new solder-interconnect built-in reliability test, FFT based statistical-pattern recognition, and time-frequency moments based statistical pattern recognition. The solder-joint built-in-reliability-test has been developed for detecting high-resistance and intermittent faults in operational, fully programmed field programmable gate arrays. Frequency band energy is computed using FFT and utilized as the classification feature to check for damage and failure in the assembly. In addition, the Time Frequency Analysis has been used to study of the energy densities of the signal in both time and frequency domain, and provide information about the time-evolution of frequency content of transient-strain signal. Closed-form models have been developed for the eigen-frequencies and mode-shapes of electronic assemblies with various boundary conditions and component placement configurations. Model predictions have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock-damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response have been quantified. Explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package falloff and solder ball failure. This allows the physical quantification of solder ball crack damage in the form of confidence values and provides a damage index that can be utilized for the health monitoring of solder interconnects in an electronic assembly.

Time-Frequency Analysis and Built-In Reliability Test for Health Monitoring of Electronics Under Shock Loads

2007 ◽  
Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameep Gupte ◽  
Prashant Gupta ◽  
Jeff Suhling ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Health Monitoring ◽  
Solder Ball ◽  
Statistical Pattern Recognition ◽  
Reliability Test ◽  
Leading Indicators ◽  
Time Frequency ◽  
Transient Strain ◽  
Statistical Pattern ◽  
Electronic Assemblies

The built-in stress test (BIST) is extensively used for diagnostics or identification of failure. The current version of BIST approach is focused on reactive failure detection and provides limited insight into reliability and residual life. A new approach has been developed to monitor product-level damage during shock and vibration. The approach focuses on the pre-failure space and methodologies for quantification of failure in electronic equipment subjected to shock and vibration loads using the dynamic response of the electronic equipment. Presented methodologies are applicable at the system-level for identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in shock and drop of electronic assemblies. Three methodologies have been investigated for feature extraction and health monitoring including development of a new solder-interconnect built-in reliability test, FFT based statistical-pattern recognition, and time-frequency moments based statistical pattern recognition. The solder-joint built-in-reliability-test has been developed for detecting high-resistance and intermittent faults in operational, fully programmed field programmable gate arrays. Frequency band energy is computed using FFT and utilized as the classification feature to check for damage and failure in the assembly. In addition, the Time Frequency Analysis has been used to study of the energy densities of the signal in both time and frequency domain, and provide information about the time-evolution of frequency content of transient-strain signal. Closed-form models have been developed for the eigen-frequencies and mode-shapes of electronic assemblies with various boundary conditions and component placement configurations. Model predictions have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock-damage to subtle changes in boundary conditions, effective flexural rigidity, and transient strain response have been quantified. Explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package falloff and solder ball failure. This allows the physical quantification of solder ball crack damage in the form of confidence values and provides a damage index that can be utilized for the health monitoring of solder interconnects in an electronic assembly.

Automated feature extraction for the classification of humanin vivo13C NMR spectra using statistical pattern recognition and wavelets

1996 ◽  
Vol 35 (6) ◽  
pp. 834-840 ◽  
Author(s):  
A. Rosemary Tate ◽  
Des Watson ◽  
Stephen Eglen ◽  
Theodores N. Arvanitis ◽  
E. Louise Thomas ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Nmr Spectra ◽  
Statistical Pattern Recognition ◽  
Statistical Pattern ◽  
Automated Feature Extraction
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