Integrating Machine and Quality Data for Predictive Maintenance in Manufacturing System

Maintenance and quality control are typically disjoint areas in a production system and even though interactions between them do exist, they are limited. In some cases, the quality deviations are reported directly by the client the product is sold to before maintenance actions are taken to repair the faulty machines and prevent these specific deviations. In this paper, we claim that by using machine and quality data in combination, it is possible to generate information about the process and the resulting product, that will allow to detect deviations in earlier stages, likely before the product reaches the client, possibly even before it is produced. We analyze a production process over a period of two years, during which operational parameters of the machines executing the process are reported, as well as the quality deviations of the parts produced. The data gathered is used to establish whether there exists a correlation between the machine status and the quality deviations of the products. Experiments show that the correlation increases when adjustments to the machines are made. This evidence supports our hypothesis of the possibility of using quality and machine data in combination in the development of future predictive maintenance solutions.