Neural network recognition of electronic malfunctions

Abstract

Neural network software can be applied to manufacturing process control as a tool for diagnosing the state of an electronic circuit board. The neural network approach significantly reduces the amount of time required to build a diagnostic system. This time reduction occurs because the ordinary combinatorial explosion in rules for identifying faulted components can be avoided. Neural networks circumvent the combinatorial explosion by taking advantage of the fact that the fault characteristics of multiple simultaneous faults frequently correlate to the fault characteristics of the individual faulted components. This article clearly demonstrates that state-of-the-art neural networks can be used in automatic test equipment for iterative diagnosis of electronic circuit board malfunctions.