Damage inspection and health monitoring of dynamical systems by advanced time-frequency analysis

2010 ◽  
Author(s):  
P. Frank Pai ◽  
Genyong Wu ◽  
Xin Qian
Keyword(s):  
Dynamical Systems ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Time Frequency Analysis ◽  
Time Frequency

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.

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.

A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring

2020 ◽  
Vol 19 (6) ◽  
pp. 1963-1975 ◽  
Author(s):  
Yuequan Bao ◽  
Yibing Guo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Structural Health ◽  
Reconstructed Signal

Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time-varying parameters. Thus, it plays a significant role in structural health monitoring, such as data analysis, and nonlinear damage detection. Adaptive sparse time–frequency analysis is a recently developed method used to estimate an instantaneous frequency, which can achieve high-resolution adaptivity by looking for the sparsest time–frequency representation of the signal within the largest possible time–frequency dictionary. However, in adaptive sparse time–frequency analysis, non-convex least-square optimization is the most important and difficult part of the algorithm; therefore, in this research the powerful optimization capabilities of machine learning were employed to solve the non-convex least-square optimization and achieve the accurate estimation of the instantaneous frequency. First, the adaptive sparse time–frequency analysis was formalized into a machine-learning task. Then, a four-layer neural network was designed, the first layer of which was used for training the coefficients of the envelope of each basic functions in a linear space. The next two merge layers were used to solve the complex calculation in a neural network. Finally, the real and imaginary parts of the reconstructed signal were the outputs of the output layer. The optimal weights in this designed neural network were trained and optimized by comparing the output reconstructed signal with the target signal, and a stochastic gradient descent optimizer was used to update the weights of the network. Finally, the numerical examples and experimental examples of a cable model were employed to illustrate the ability of the proposed method. The results show that the proposed method which is called neural network–adaptive sparse time–frequency analysis can give accurate identification of the instantaneous frequency, and it has a better robustness to initial values when compared with adaptive sparse time–frequency analysis.

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