Remaining Useful Life Estimation and Prognostication of SAC305 PCB's for Dynamic Conditions of Temperature and Vibration Loads

Abstract

Abstract This study focuses on the feature vector identification and Remaining Useful Life (RUL) estimation of SAC305 solder alloy PCB's of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels are investigated. The remaining Useful Life of the packages was estimated using a deep learning approach based on Long Short Term Memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The autocorrelation function's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.