A Soft Computing Approach for Fault Prediction of Electronic Systems

This paper presents a soft computing approach for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor – susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For online prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardization is fed into the neuro-fuzzy model. For comparison purpose, we also trained a artificial neural network using backpropagation learning and evaluated the comparative performance. The performance of the proposed method of prediction is evaluated by comparing the experimental results with the actual failure model values. Test results reveal that neuro-fuzzy models outperformed neural network in terms of performance time and error achieved.