Application of Grey Prediction Model for Failure Prognostics of Electronics
Volume 6, Number 5, September 2010 - Paper 3 - pp. 435-442
JIE GU1, NIKHIL VICHARE2, BILAL AYYUB3, MICHAEL PECHT41 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, U.S.A.
2 Portables Reliability, Dell Inc., U.S.A
3 Center for Technology and Systems Management (CTSM), University of Maryland U.S.A.
4 Visiting Professor of Electrical Engineering, City University of Hong Kong and Director, CALCE, University of Maryland U.S.A.
(Received on August 31, 2009, revised on March 21, 2010)
Reliability prediction is becoming more and more important for electronics components and devices, such as avionics. In this paper, a grey prediction model based prognostics approach was developed to perform the failure prediction of electronics. The grey prediction model first makes the original data set into a new data set with less randomness in order to find the tendency. Then, history data is needed for training the algorithm and predicting the future condition. Last, the predicted result in the new data set is transferred back to the original data set. Compared with traditional data-driven method, this approach was especially useful for reliability prediction with small sample size. The whole prognostics approach was also verified by two case studies. One was performed on electronic boards with ball grid array (BGA) and quad flat package (QFP) components under thermal cycle loading. The other was performed on electronic boards with capacitors under temperature, humidity and bias tests.
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