Anomaly Detection in Spot Welding Machines in the Automotive Industry for Maintenance Prioritization

Abstract

Based on the need of prioritization of maintenance activities in a BOSCH Spot Welding process in the automotive industry, this work aims to develop anomalous equipment selection methodologies for assisting it. The first one is proposed based on data exploration by checking every possible set of alarms of the machines. A second one is created using multiple data clustering models in order to identify machines that behave differently from the others for certain time periods. Bayesian networks were also applied to assist the identification of cause-and-effect relationships between the warning and error logs. The clustering method proved effective in identifying anomalies, which were later inspected on the shop floor.