Customer failure modes prediction for hard disk drive using neural networks rank-level fusion

Abstract

The Prediction of Customer Failure Modes in Hard Disk Drive (HDD) is proposed using Neural Networks Rank-Level Fusion applied on key parameters measured in the manufacturing process of a HDD. In our methods, Neural Networks, Discriminant Analysis, Bayesian Networks, Support Vector Machines are applied to classified data which was obtained from Principal Component Analysis. The output of the classifiers is further aggregated using Neural Networks Rank-Level Fusion to form the final prediction model. The resultant of the model is a highly accurate prediction superior to Borda Count, Logistic Regression Fusion Methods and beyond current known reliability predictors of HDD failures. © 2011 IEEE.