Image and Inspection Data Analysis to Group Electrical Components for Correlation With Remaining Useful Life

In safety-critical applications such as automotive or railway technology, prediction of the condition and remaining life of electronic components is a major challenge. To determine the condition, a comprehensive digital fingerprint of the components is needed, which already starts during the production phase. Currently, inspection data such as SPI, AOI and AXI data are used to group the components into two groups ("OK" and "not OK"). In the future, however, this inspection data will also be used to predict the service life and current condition of the assembly. This article deals with the investigation and processing of the imaging inspection data, as well as the geometry parameters determined from it. First, a dimensional reduction of the input data is carried out with the help of mathematical methods (PCA, t-SNE and Autoencoder). This reduction extracts the relevant information from the input data from the point of view of the AI. So far, clustering methods such as K-Means, DBSCAN and Hierarchical Clustering have been used to generate groups. In the future, these groupings can be used to predict the service life together with the loads on the components and thus make a statement about the current condition of the components