Health Monitoring of PCB’s Under Mechanical Shock Loads

2019 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Jeff Suhling ◽  
Ken Blecker
Keyword(s):  
Health Monitoring ◽  
Failure Strain ◽  
Mechanical Shock ◽  
Frequency Data ◽  
Time Frequency ◽  
Feature Vectors ◽  
Damping Characteristics ◽  
Before And After ◽  
Frequency Components ◽  
Electronic Assemblies

Abstract Feature vectors for health monitoring of electronic assemblies under repetitive mechanical shock have been developed for assemblies subject to 3,000g acceleration levels. The resistance and strain measurements of the PCB are acquired during each drop to analyze the changes in the values during the experiment. Analysis on the progression of failure was carried out using frequency-based techniques on the strain signals from different locations of the board and failure of the package was identified from the increase in the resistance values of the package during the drop. Feature vectors selected were based on the time-frequency data as well as the logarithmic decrement of the strain signals during the different drops. Different statistical approaches on identifying the changes in the damping characteristics of the package during drop were also carried out. Statistical analysis on the changes in the resistance values were quantified in accordance with the changes in the strain and correlation of the both were attempted. The dependence on position of the strain gauge on the PCB were also studied by comparing the variation of the feature vectors of the corresponding strain signals. The before and after failure strain signals were compared on the frequency components and as well as the changes in the damping characteristics of the strain signals.

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.

Anomaly-Detection and Prognostication of Electronics Subjected to Shock and Vibration

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Arjun Angral ◽  
Jeff Suhling
Keyword(s):  
Experimental Data ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Solder Ball ◽  
Failure Modes ◽  
Feature Space ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Fault Mode ◽  
Electronic Assemblies

Failures in electronics subjected to shock and vibration are typically diagnosed using the built-in self test (BIST) or using continuity monitoring of daisy-chained packages. The BIST which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. In this paper, a new technique has been developed for health monitoring and failure mode classification based on measured damage precursors. A feature extraction technique in the joint-time frequency domain has been developed along with pattern classifiers for fault diagnosis of electronics at product-level. The Karhunen Loe´ve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for fault mode classification in electronic assemblies. Euclidean, and Mahalanobis, and Bayesian distance classifiers based on joint-time frequency analysis, have been used for classification of the resulting feature space. Previously, the authors have developed damage pre-cursors based on time and spectral techniques for health monitoring of electronics without reliance on continuity data from daisy-chained packages. Statistical Pattern Recognition techniques based on wavelet packet energy decomposition [Lall 2006a] have been studied by authors for quantification of shock damage in electronic assemblies, and auto-regressive moving average, and time-frequency techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems [Lall 2008]. However, identification of specific failure modes was not possible. In this paper, various fault modes such as solder inter-connect failure, inter-connect missing, chip delamination chip cracking etc in various packaging architectures have been classified using clustering of feature vectors based on the KLT approach [Goumas 2002]. The KLT de-correlates the feature space and identifies dominant directions to describe the space, eliminating directions that encode little useful information about the features [Qian 1996, Schalkoff 1972, Theodoridis 1998, Tou 1974]. The clustered damage pre-cursors have been correlated with underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC105, SAC305 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. Fault-mode classification has been done using KLT and joint-time-frequency analysis of the experimental data. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Models using cohesive elements present at the solder joint-copper pad interface at both the PCB and package side have also been created to study the traction-separation behavior of solder. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental 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.

Feature Vector Based Failure Mode Identification and Prognostication of Electronics Subjected to Shock and Vibration

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Arjun Angral ◽  
Jeff Suhling
Keyword(s):  
Experimental Data ◽  
Failure Mode ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Solder Ball ◽  
Failure Modes ◽  
Feature Space ◽  
Time Frequency ◽  
Fault Mode ◽  
Electronic Assemblies

Failures in electronics subjected to shock and vibration are typically diagnosed using the built-in self test (BIST) or using continuity monitoring of daisy-chained packages. The BIST which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. In this paper, a new technique has been developed for health monitoring and failure mode classification based on measured damage precursors. A feature extraction technique in the joint-time frequency domain has been developed along with pattern classifiers for fault diagnosis of electronics at product-level. The Karhunen Loe´ve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for fault mode classification in electronic assemblies. Euclidean, and Mahalanobis, and Bayesian distance classifiers based on joint-time frequency analysis, have been used for classification of the resulting feature space. Previously, the authors have developed damage pre-cursors based on time and spectral techniques for health monitoring of electronics without reliance on continuity data from daisy-chained packages. Statistical Pattern Recognition techniques based on wavelet packet energy decomposition [Lall 2006a] have been studied by authors for quantification of shock damage in electronic assemblies, and auto-regressive moving average, and time-frequency techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems [Lall 2008]. However, identification of specific failure modes was not possible. In this paper, various fault modes such as solder inter-connect failure, inter-connect missing, chip delamination chip cracking etc in various packaging architectures have been classified using clustering of feature vectors based on the KLT approach [Goumas 2002]. The KLT de-correlates the feature space and identifies dominant directions to describe the space, eliminating directions that encode little useful information about the features [Qian 1996, Schalkoff 1972, Theodoridis 1998, Tou 1974]. The clustered damage pre-cursors have been correlated with underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC105, SAC305 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. Fault-mode classification has been done using KLT and joint-time-frequency analysis of the experimental data. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Models using cohesive elements present at the solder joint-copper pad interface at both the PCB and package side have also been created to study the traction-separation behavior of solder. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals A Vibration-Based Structural Health Monitoring System for Transmission Line Towers

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 515 ◽  
Author(s):  
Long Zhao ◽  
Xinbo Huang ◽  
Ye Zhang ◽  
Yi Tian ◽  
Yu Zhao
Keyword(s):  
Structural Health Monitoring ◽  
Transmission Line ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Structural Changes ◽  
Natural Frequencies ◽  
Transmission Tower ◽  
Health Monitoring System ◽  
Wind Speeds ◽  
Before And After

In this paper, we present a vibration-based transmission tower structural health monitoring system consisting of two parts that identifies structural changes in towers. An accelerometer group realizes vibration response acquisition at different positions and reduces the risk of data loss by data compression technology. A solar cell provides the power supply. An analyser receives the data from the acceleration sensor group and calculates the transmission tower natural frequencies, and the change in the structure is determined based on natural frequencies. Then, the data are sent to the monitoring center. Furthermore, analysis of the vibration signal and the calculation method of natural frequencies are proposed. The response and natural frequencies of vibration at different wind speeds are analysed by time-domain signal, power spectral density (PSD), root mean square (RMS) and short-time Fouier transform (STFT). The natural frequency identification of the overall structure by the stochastic subspace identification (SSI) method reveals that the number of natural frequencies that can be calculated at different wind speeds is different, but the 2nd, 3rd and 4th natural frequencies can be excited. Finally, the system was tested on a 110 kV experimental transmission line. After 18 h of experimentation, the natural frequency of the overall structure of the transmission tower was determined before and after the tower leg was lifted. The results show that before and after the tower leg is lifted, the natural frequencies of each order exhibit obvious changes, and the differences in the average values can be used as the basis for judging the structural changes of the tower.

Sign in / Sign up

Export Citation Format

Share Document