Prognostics using Kalman-Filter models and metrics for risk assessment in BGAs under shock and vibration loads

Structural damage to BGA interconnects incurred during vibration testing has been monitored in the pre-failure space using resistance spectroscopy based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss-of-functionality are too high to bear. Future state of the system has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostication health management algorithm during the vibration test has been quantified using performance evaluation metrics. The methodology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α-λ metric, sample standard deviation, mean square error, mean absolute percentage error, average bias, relative accuracy, and cumulative relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.