Health Monitoring for Damage Initiation & Progression during Mechanical Shock in Electronic Assemblies

2006 ◽  
Author(s):  
P. Lall ◽  
P. Choudhary ◽  
S. Gupte ◽  
J. Suhling
Keyword(s):  
Health Monitoring ◽  
Mechanical Shock ◽  
Damage Initiation ◽  
Electronic Assemblies

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.

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.

KL Transform and Neural-Net Based Framework for Failure Modes Classification in Electronics Subjected to Mechanical-Shock

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Kai Goebel
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Feature Space ◽  
System Level ◽  
Neural Net ◽  
Mechanical Shock ◽  
Time Frequency ◽  
Damage Initiation

Electronic systems under extreme shock and vibration environments including shock and vibration may sustain several failure modes simultaneously. Previous experience of the authors indicates that the dominant failure modes experienced by packages in a drop and shock frame work are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loe´ve transform with parity-based stepwise supervised training of a perceptrons. Early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with Karhunen Loe´ve transform is new. Feature space has been formed by joint time frequency analysis. Since the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. Error Back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure modes in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hoteling’s T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of specific dominant failure mode and is scalable to system level reliability.

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.

Enabling damage detection: manufacturing composite laminates doped with dispersed triboluminescent materials

2011 ◽  
Vol 30 (22) ◽  
pp. 1869-1876 ◽  
Author(s):  
Tarik J. Dickens ◽  
Okenwa I. Okoli
Keyword(s):  
Tensile Strength ◽  
Health Monitoring ◽  
Composite Laminates ◽  
Curing Kinetics ◽  
Processing Parameters ◽  
In Situ Monitoring ◽  
Mechanical Agitation ◽  
Damage Initiation ◽  
Vacuum Infusion ◽  
Monitoring Technologies

Triboluminescent materials are being harnessed to address the gaps in current structural health monitoring systems. Their innate ability to emit light when stressed or broken makes them ideal candidates for the ubiquitous and in situ monitoring of structures. The increasing use of advanced composites in critical structures, where subsurface damage initiation may go unnoticed, further highlights the urgency in developing efficient online monitoring technologies. This work looked at the manufacturing of composite laminates that have been doped with various concentrations (0 to 10 %wt.) of a triboluminescent material (ZnS:Mn). Laminates were manufactured using a vacuum infusion process. Dispersing the ZnS:Mn particulates was cumbersome because their density was higher than the resin that caused settling during resin infusion. The dispersion of ZnS:Mn is critical to their use in the health monitoring of the host structure. As such, a method for mechanical agitation using a rotational vacuum infusion apparatus was developed employing centrifugal motion. The degree of dispersion in the resulting laminates was determined using scanning electron microscopy and the energy dispersive scanning feature of the electron microscope for elemental mapping. A quantitative metric was established by computations of the Euclidean distance of EDS mapping. Studies of the effect of ZnS:Mn concentration on the tensile strength of laminates showed that increasing the ZnS:Mn concentration reduced the tensile strength. Key processing parameters were studied, and determined that curing kinetics were not altered by ZnS:Mn inclusion.

PHM of Leadfree Electronic Assemblies Using Resistance Spectroscopy With Sequential Importance Resampling

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
State Space ◽  
Feature Vector ◽  
Health Management ◽  
Residual Life ◽  
The State ◽  
Remaining Useful Life ◽  
Damage Initiation ◽  
State Variable ◽  
Useful Life ◽  
Electronic Assemblies

Electronic assemblies have been monitored using state-space vectors from resistance spectroscopy, phase-sensitive detection and particle filtering (PF) to quantify damage initiation, progression and remaining useful life of the electronic assembly. A prognostication health management (PHM) methodology has been presented for electronic components subjected to mechanical shock and vibration. The presented methodology is an advancement of the state-of-art, which presently focuses on reactive failure detection and provides limited or no insight into the system reliability and residual life. Previously damage initiation, damage progression, and residual life in the pre-failure space has been correlated with micro-structural damage based proxies, feature vectors based on time, spectral and joint time-frequency characteristics of electronics [Lall2004a–d, 2005a–b, 2006a–f, 2007a–e, 2008a–f]. Precise resistance measurements based on the resistance spectroscopy method have been used to monitor interconnects for damage and prognosticate failure [Lall 2009a,b, 2010a,b, Constable 1992, 2001]. In this paper, the effectiveness of the proposed particle filter and resistance spectroscopy based approach in a prognostic health management (PHM) framework has been demonstrated for electronics. The measured state variable has been related to the underlying damage state using non-linear finite element analysis. The particle filter has been used to estimate the state variable, rate of change of the state variable, acceleration of the state variable and construct a feature vector. The estimated state-space parameters have been used to extrapolate the feature vector into the future and predict the time-to-failure at which the feature vector will cross the failure threshold. Remaining useful life has been calculated based on the evolution of the state space feature vector. Standard prognostic health management metrics were used to quantify the performance of the algorithm against the actual remaining useful life. Application to part replacement decisions for ultra-high reliability system has been demonstrated. Using the technique described in the paper the appropriate time to reorder a replacement part could be monitored, and defended statistically. Robustness of the prognostication algorithm has been quantified using standard performance evaluation metrics.

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.

Structural Health Monitoring of Steel Frame Structure by Experimental Modal Parameter Identification

2020 ◽  
Vol 37 ◽  
pp. 1-13
Author(s):  
Bulbul Ahmed ◽  
Florea Dinu ◽  
Ioan Marginean
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Frame ◽  
Parameters Estimation ◽  
Operating Conditions ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Damage Initiation ◽  
Structural Health ◽  
Lower Beam

Structural health monitoring (SHM) is a modern technique f or damage identification in the e xis ting structure. The structural stiffness, frequency, damping, and dominant mode shapes represent the actual operating conditions of the structure. The main principle of structural health monitoring is to identif y the mod al parameters from experime ntal resu lts both damaged and undamaged conditions. Damage is much effective to decrease stiffness and strength of structural components and it changes dynamic behaviour and damping ratio of whole structures. Bruel & Kjaer experi mental modal analys is technique is r ecently used for civil engineering structures for modal parameters estimation. The paper describes the initial structural health monitoring of a steel frame . The modal parameters were estimated for undamaged condition s a nd these result s are verified and up dated by the numerical FEM tool SAP2000. For the undamaged structure , mode shapes and frequencies were calibrated properly. In the second step, damaged was initiated by dismantling one element from the lower part of the frame. The estimat ed m odal parameter s were compared to the initial one. The mode shapes and frequencies are quite different for some specific mode due to damage initiation . One extra mode was created for the damaged frame due to damage initiation. The 4 th mode was not found f or the initial m easurement because of presence of lower beam. Lower beam restraints the 4 th mode and the frame behaves more flexible. Keywords: SHM , Modal parameters, FEM modelling, Damage characterization, Experimental mo dal analysis (EMA) .

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.

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