Board-Level Functional Fault Diagnosis Using Learning Based on Incremental Support-Vector Machines

Advanced machine learning techniques offer an unprecedented opportunity to increase the accuracy of board-level functional fault diagnosis based on the historical data of successfully repaired boards. However, the training complexity increases significantly in diagnosis systems due to the increasing amount of the historical data. We propose a smart learning method in the diagnosis system using incremental support-vector machines (SVMs). The SVMs updated using incremental learning allow the diagnosis system to quickly adapt to new error observations and provide more accurate fault diagnosis. Two sets of large-scale synthetic data generated from the log information of two complex industrial boards, in volume production, are used to validate the proposed diagnosis approach in terms of training time and diagnosis accuracy over a previously proposed diagnosis system based on simple support-vector machines.