Comparison of Lalman-filter and extended Kalman-filter for prognostics health management of electronics

Electronic interconnects may encounter damage under exposure to vibration and mechanical shock. The damage may manifest itself as an open circuit after a finite period of operation. The traditional methods for damage detection such as microscopy and x-ray are destructive in nature and provide limited information offline. In this paper, resistance spectroscopy and phase-sensitive detection based state space vectors have been used to monitor the health of interconnects and assess remaining useful life. Two techniques including Kalman Filter and Extended Kalman Filter have been used to estimate the future state of the system. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of both the prognostication health management algorithms during vibration and mechanical shock has been quantified using performance evaluation metrics. The methodology has been demonstrated on lead-free area-array and advanced interconnect electronic assemblies. 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.