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

Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameep Gupte ◽  
Prashant Gupta ◽  
Jeff Suhling ◽  
...  

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.

Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameep Gupte ◽  
Prashant Gupta ◽  
Jeff Suhling ◽  
...  

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.


Author(s):  
Pradeep Lall ◽  
Prakriti Choudhary ◽  
Sameed Gupta ◽  
Jeff Suhling

Electronic products may be subjected shock and vibration during shipping, normal usage and accidental drop. High-strain rate transient bending produced by such loads may result in failure of fine-pitch electronics. Current experimental techniques rely on electrical resistance for determination of failure. Significant advantage can be gained by prior knowledge of impending failure for applications where the consequences of system-failure may be catastrophic. This research effort focuses on an alternate approach to damage-quantification in electronic assemblies subjected to shock and vibration, without testing for electrical continuity. The proposed approach can be extended to monitor product-level damage. In this paper, statistical pattern recognition and leading indicators of shock-damage have been used to study the damage initiation and progression in shock and drop of electronic assemblies. Statistical pattern recognition is currently being employed in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, artificial intelligence, computer vision and remote sensing [Jain, et. al. 2000]. The application quantification of shock damage in electronic assemblies is new. Previously, free vibration of rectangular plates has been studied by various researchers [Leissa 1969, Young 1950, Gorman 1982, Gurgoze 1984, Wu 2003] for development of analytical closed-form models. In this paper, 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. A damage index for Experimental Damage Monitoring has been developed using the failure indicators. The above damage monitoring approach is not based on electrical continuity and hence can be applied to any electronic assembly structure irrespective of the interconnections. The damage index developed provides parametric damage progression data, thus removing the limitation of current failure testing, where the damage progression can not be monitored. Hence the proposed method does not require the assumption that the failure occurs abruptly after some number of drops and can be extended to product level drops.


Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Arjun Angral ◽  
Jeff Suhling

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.


Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani

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.


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