Prognostics and condition monitoring of electronics

In this paper, a leading indicators based approach has been developed for prognostics and health monitoring of electronic systems. The approach focuses on the prefailure space and methodologies for quantification of damage progression and residual life in electronic equipment subjected to shock and vibration loads using the dynamic response of the electronic equipment. Traditional health monitoring methodologies have relied on reactive methods of failure detection often providing little or no insight into the remaining useful life of the system. The proposed techniques have a wide-applicability to electronic systems requiring high reliability. Operational readiness and high-system availability are critical for reduction of uncertainty in mission-critical electronic systems. Examples include aerospace-electronic systems which usually face a very harsh environment, requiring them to survive the high strain rates, e.g. during launch and re-entry and thermal environments including extreme low and high temperatures and implantable biological systems such as pacemakers and defibrillators. Prognostic indicators can trigger preventive maintenance based on need instead of ldquofear-of-failurerdquo. Auto-regressive (AR), wavelet packet energy decomposition, and time-frequency (TFA) techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems. The test vehicle subjected to repeated proof-load events consists of a wide frequency range. In this approach the known auto correlation sequence of the feature vectors, is extrapolated to estimate auto correlation sequence at unknown lags. One of the main advantages of the AR technique is that it is primarily a signal based technique. Reduced reliance on system analysis helps avoid errors which otherwise may render the process of fault detection and diagnosis quite complex and dependent on the skills of the analyst. Results of the present study show that the A- R and TFA based health monitoring techniques are feasible for fault detection and damage-assessment in electronic units. 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.