Assessment of the possibility of machine learning for electronic equipment quality prediction

The major algorithms and methods of machine learning are considered. A possibility of machine learning and neural network using for electronic equipment quality prediction is assessed. The paper provides examples of the successful application of these algorithms to improve such quality of electronic components indicators as reliability, resistance to external influencing factors, etc. Before testing electronic components on resistance to external influencing factors it is necessary to identify samples of electronic components by fluoroscopy in order to identify possible heterogeneity in the structure of samples belonging to the same batch. A solution of the electronic components batches uniformity problem using computer vision and clustering algorithms is proposed.